tesseract: tesseract::Classify Class Reference

#include <classify.h>

Inheritance diagram for tesseract::Classify:

Public Member Functions
	Classify ()

virtual	~Classify ()

Dict &	getDict ()

const ShapeTable *	shape_table () const

void	SetStaticClassifier (ShapeClassifier *static_classifier)

void	AddLargeSpeckleTo (int blob_length, BLOB_CHOICE_LIST *choices)

bool	LargeSpeckle (const TBLOB &blob)

ADAPT_TEMPLATES	NewAdaptedTemplates (bool InitFromUnicharset)

int	GetFontinfoId (ADAPT_CLASS Class, uinT8 ConfigId)

int	PruneClasses (const INT_TEMPLATES_STRUCT int_templates, int num_features, int keep_this, const INT_FEATURE_STRUCT features, const uinT8 normalization_factors, const uinT16 expected_num_features, GenericVector< CP_RESULT_STRUCT > *results)

void	ReadNewCutoffs (FILE *CutoffFile, bool swap, inT64 end_offset, CLASS_CUTOFF_ARRAY Cutoffs)

void	PrintAdaptedTemplates (FILE *File, ADAPT_TEMPLATES Templates)

void	WriteAdaptedTemplates (FILE *File, ADAPT_TEMPLATES Templates)

ADAPT_TEMPLATES	ReadAdaptedTemplates (FILE *File)

FLOAT32	ComputeNormMatch (CLASS_ID ClassId, const FEATURE_STRUCT &feature, BOOL8 DebugMatch)

void	FreeNormProtos ()

NORM_PROTOS *	ReadNormProtos (FILE *File, inT64 end_offset)

void	ConvertProto (PROTO Proto, int ProtoId, INT_CLASS Class)

INT_TEMPLATES	CreateIntTemplates (CLASSES FloatProtos, const UNICHARSET &target_unicharset)

void	LearnWord (const char fontname, WERD_RES word)

void	LearnPieces (const char fontname, int start, int length, float threshold, CharSegmentationType segmentation, const char correct_text, WERD_RES *word)

void	InitAdaptiveClassifier (bool load_pre_trained_templates)

void	InitAdaptedClass (TBLOB *Blob, CLASS_ID ClassId, int FontinfoId, ADAPT_CLASS Class, ADAPT_TEMPLATES Templates)

void	AmbigClassifier (const GenericVector< INT_FEATURE_STRUCT > &int_features, const INT_FX_RESULT_STRUCT &fx_info, const TBLOB blob, INT_TEMPLATES templates, ADAPT_CLASS classes, UNICHAR_ID ambiguities, ADAPT_RESULTS results)

void	MasterMatcher (INT_TEMPLATES templates, inT16 num_features, const INT_FEATURE_STRUCT features, const uinT8 norm_factors, ADAPT_CLASS classes, int debug, int matcher_multiplier, const TBOX &blob_box, const GenericVector< CP_RESULT_STRUCT > &results, ADAPT_RESULTS final_results)

void	ExpandShapesAndApplyCorrections (ADAPT_CLASS classes, bool debug, int class_id, int bottom, int top, float cp_rating, int blob_length, int matcher_multiplier, const uinT8 cn_factors, UnicharRating int_result, ADAPT_RESULTS final_results)

double	ComputeCorrectedRating (bool debug, int unichar_id, double cp_rating, double im_rating, int feature_misses, int bottom, int top, int blob_length, int matcher_multiplier, const uinT8 *cn_factors)

void	ConvertMatchesToChoices (const DENORM &denorm, const TBOX &box, ADAPT_RESULTS Results, BLOB_CHOICE_LIST Choices)

void	AddNewResult (const UnicharRating &new_result, ADAPT_RESULTS *results)

int	GetAdaptiveFeatures (TBLOB Blob, INT_FEATURE_ARRAY IntFeatures, FEATURE_SET FloatFeatures)

void	DebugAdaptiveClassifier (TBLOB Blob, ADAPT_RESULTS Results)

PROTO_ID	MakeNewTempProtos (FEATURE_SET Features, int NumBadFeat, FEATURE_ID BadFeat[], INT_CLASS IClass, ADAPT_CLASS Class, BIT_VECTOR TempProtoMask)

int	MakeNewTemporaryConfig (ADAPT_TEMPLATES Templates, CLASS_ID ClassId, int FontinfoId, int NumFeatures, INT_FEATURE_ARRAY Features, FEATURE_SET FloatFeatures)

void	MakePermanent (ADAPT_TEMPLATES Templates, CLASS_ID ClassId, int ConfigId, TBLOB *Blob)

void	PrintAdaptiveMatchResults (const ADAPT_RESULTS &results)

void	RemoveExtraPuncs (ADAPT_RESULTS *Results)

void	RemoveBadMatches (ADAPT_RESULTS *Results)

void	SetAdaptiveThreshold (FLOAT32 Threshold)

void	ShowBestMatchFor (int shape_id, const INT_FEATURE_STRUCT *features, int num_features)

STRING	ClassIDToDebugStr (const INT_TEMPLATES_STRUCT *templates, int class_id, int config_id) const

int	ClassAndConfigIDToFontOrShapeID (int class_id, int int_result_config) const

int	ShapeIDToClassID (int shape_id) const

UNICHAR_ID *	BaselineClassifier (TBLOB Blob, const GenericVector< INT_FEATURE_STRUCT > &int_features, const INT_FX_RESULT_STRUCT &fx_info, ADAPT_TEMPLATES Templates, ADAPT_RESULTS Results)

int	CharNormClassifier (TBLOB blob, const TrainingSample &sample, ADAPT_RESULTS adapt_results)

int	CharNormTrainingSample (bool pruner_only, int keep_this, const TrainingSample &sample, GenericVector< UnicharRating > *results)

UNICHAR_ID *	GetAmbiguities (TBLOB *Blob, CLASS_ID CorrectClass)

void	DoAdaptiveMatch (TBLOB Blob, ADAPT_RESULTS Results)

void	AdaptToChar (TBLOB *Blob, CLASS_ID ClassId, int FontinfoId, FLOAT32 Threshold, ADAPT_TEMPLATES adaptive_templates)

void	DisplayAdaptedChar (TBLOB blob, INT_CLASS_STRUCT int_class)

bool	AdaptableWord (WERD_RES *word)

void	EndAdaptiveClassifier ()

void	SettupPass1 ()

void	SettupPass2 ()

void	AdaptiveClassifier (TBLOB Blob, BLOB_CHOICE_LIST Choices)

void	ClassifyAsNoise (ADAPT_RESULTS *Results)

void	ResetAdaptiveClassifierInternal ()

void	SwitchAdaptiveClassifier ()

void	StartBackupAdaptiveClassifier ()

int	GetCharNormFeature (const INT_FX_RESULT_STRUCT &fx_info, INT_TEMPLATES templates, uinT8 pruner_norm_array, uinT8 char_norm_array)

void	ComputeCharNormArrays (FEATURE_STRUCT norm_feature, INT_TEMPLATES_STRUCT templates, uinT8 char_norm_array, uinT8 pruner_array)

bool	TempConfigReliable (CLASS_ID class_id, const TEMP_CONFIG &config)

void	UpdateAmbigsGroup (CLASS_ID class_id, TBLOB *Blob)

bool	AdaptiveClassifierIsFull () const

bool	AdaptiveClassifierIsEmpty () const

bool	LooksLikeGarbage (TBLOB *blob)

void	RefreshDebugWindow (ScrollView *win, const char msg, int y_offset, const TBOX &wbox)

void	ClearCharNormArray (uinT8 *char_norm_array)

void	ComputeIntCharNormArray (const FEATURE_STRUCT &norm_feature, uinT8 *char_norm_array)

void	ComputeIntFeatures (FEATURE_SET Features, INT_FEATURE_ARRAY IntFeatures)

INT_TEMPLATES	ReadIntTemplates (FILE *File)

void	WriteIntTemplates (FILE *File, INT_TEMPLATES Templates, const UNICHARSET &target_unicharset)

CLASS_ID	GetClassToDebug (const char Prompt, bool adaptive_on, bool pretrained_on, int shape_id)

void	ShowMatchDisplay ()

UnicityTable< FontInfo > &	get_fontinfo_table ()

const UnicityTable< FontInfo > &	get_fontinfo_table () const

UnicityTable< FontSet > &	get_fontset_table ()

void	NormalizeOutlines (LIST Outlines, FLOAT32 XScale, FLOAT32 YScale)

FEATURE_SET	ExtractOutlineFeatures (TBLOB *Blob)

FEATURE_SET	ExtractPicoFeatures (TBLOB *Blob)

FEATURE_SET	ExtractIntCNFeatures (const TBLOB &blob, const INT_FX_RESULT_STRUCT &fx_info)

FEATURE_SET	ExtractIntGeoFeatures (const TBLOB &blob, const INT_FX_RESULT_STRUCT &fx_info)

void	LearnBlob (const STRING &fontname, TBLOB Blob, const DENORM &cn_denorm, const INT_FX_RESULT_STRUCT &fx_info, const char blob_text)

bool	WriteTRFile (const STRING &filename)

Public Member Functions inherited from tesseract::CCStruct
	CCStruct ()

	~CCStruct ()

Public Member Functions inherited from tesseract::CUtil
	CUtil ()

	~CUtil ()

void	read_variables (const char *filename, bool global_only)

Public Member Functions inherited from tesseract::CCUtil
	CCUtil ()

virtual	~CCUtil ()

void	main_setup (const char argv0, const char basename)
	CCUtil::main_setup - set location of tessdata and name of image. More...

ParamsVectors *	params ()

Static Public Member Functions
static void	SetupBLCNDenorms (const TBLOB &blob, bool nonlinear_norm, DENORM bl_denorm, DENORM cn_denorm, INT_FX_RESULT_STRUCT *fx_info)

static void	ExtractFeatures (const TBLOB &blob, bool nonlinear_norm, GenericVector< INT_FEATURE_STRUCT > bl_features, GenericVector< INT_FEATURE_STRUCT > cn_features, INT_FX_RESULT_STRUCT results, GenericVector< int > outline_cn_counts)

Public Attributes
bool	allow_blob_division = true

bool	prioritize_division = FALSE

int	tessedit_single_match = FALSE

bool	classify_enable_learning = true

int	classify_debug_level = 0

int	classify_norm_method = character

double	classify_char_norm_range = 0.2

double	classify_min_norm_scale_x = 0.0

double	classify_max_norm_scale_x = 0.325

double	classify_min_norm_scale_y = 0.0

double	classify_max_norm_scale_y = 0.325

double	classify_max_rating_ratio = 1.5

double	classify_max_certainty_margin = 5.5

bool	tess_cn_matching = 0

bool	tess_bn_matching = 0

bool	classify_enable_adaptive_matcher = 1

bool	classify_use_pre_adapted_templates = 0

bool	classify_save_adapted_templates = 0

bool	classify_enable_adaptive_debugger = 0

bool	classify_nonlinear_norm = 0

int	matcher_debug_level = 0

int	matcher_debug_flags = 0

int	classify_learning_debug_level = 0

double	matcher_good_threshold = 0.125

double	matcher_reliable_adaptive_result = 0.0

double	matcher_perfect_threshold = 0.02

double	matcher_bad_match_pad = 0.15

double	matcher_rating_margin = 0.1

double	matcher_avg_noise_size = 12.0

int	matcher_permanent_classes_min = 1

int	matcher_min_examples_for_prototyping = 3

int	matcher_sufficient_examples_for_prototyping = 5

double	matcher_clustering_max_angle_delta = 0.015

double	classify_misfit_junk_penalty = 0.0

double	rating_scale = 1.5

double	certainty_scale = 20.0

double	tessedit_class_miss_scale = 0.00390625

double	classify_adapted_pruning_factor = 2.5

double	classify_adapted_pruning_threshold = -1.0

int	classify_adapt_proto_threshold = 230

int	classify_adapt_feature_threshold = 230

bool	disable_character_fragments = TRUE

double	classify_character_fragments_garbage_certainty_threshold = -3.0

bool	classify_debug_character_fragments = FALSE

bool	matcher_debug_separate_windows = FALSE

char *	classify_learn_debug_str = ""

int	classify_class_pruner_threshold = 229

int	classify_class_pruner_multiplier = 15

int	classify_cp_cutoff_strength = 7

int	classify_integer_matcher_multiplier = 10

INT_TEMPLATES	PreTrainedTemplates

ADAPT_TEMPLATES	AdaptedTemplates

ADAPT_TEMPLATES	BackupAdaptedTemplates

BIT_VECTOR	AllProtosOn

BIT_VECTOR	AllConfigsOn

BIT_VECTOR	AllConfigsOff

BIT_VECTOR	TempProtoMask

bool	EnableLearning

NORM_PROTOS *	NormProtos

UnicityTable< FontInfo >	fontinfo_table_

UnicityTable< FontSet >	fontset_table_

int	il1_adaption_test = 0

bool	classify_bln_numeric_mode = 0

double	speckle_large_max_size = 0.30

double	speckle_rating_penalty = 10.0

Public Attributes inherited from tesseract::CCUtil
STRING	datadir

STRING	imagebasename

STRING	lang

STRING	language_data_path_prefix

TessdataManager	tessdata_manager

UNICHARSET	unicharset

UnicharAmbigs	unichar_ambigs

STRING	imagefile

STRING	directory

char *	m_data_sub_dir = "tessdata/"

int	ambigs_debug_level = 0

bool	use_definite_ambigs_for_classifier = 0

bool	use_ambigs_for_adaption = 0

Protected Attributes
IntegerMatcher	im_

FEATURE_DEFS_STRUCT	feature_defs_

ShapeTable *	shape_table_

Additional Inherited Members
Static Public Attributes inherited from tesseract::CCStruct
static const double	kDescenderFraction = 0.25

static const double	kXHeightFraction = 0.5

static const double	kAscenderFraction = 0.25

static const double	kXHeightCapRatio

Detailed Description

Definition at line 61 of file classify.h.

Constructor & Destructor Documentation

◆ Classify()

tesseract::Classify::Classify ( )

Definition at line 35 of file classify.cpp.

     : BOOL_MEMBER(allow_blob_division, true, "Use divisible blobs chopping",
                   this->params()),
       BOOL_MEMBER(prioritize_division, FALSE,
                   "Prioritize blob division over chopping", this->params()),
       INT_MEMBER(tessedit_single_match, FALSE, "Top choice only from CP",
                  this->params()),
       BOOL_MEMBER(classify_enable_learning, true, "Enable adaptive classifier",
                   this->params()),
       INT_MEMBER(classify_debug_level, 0, "Classify debug level",
                  this->params()),
       INT_MEMBER(classify_norm_method, character, "Normalization Method   ...",
                  this->params()),
       double_MEMBER(classify_char_norm_range, 0.2,
                     "Character Normalization Range ...", this->params()),
       double_MEMBER(classify_min_norm_scale_x, 0.0, "Min char x-norm scale ...",
                     this->params()), /* PREV DEFAULT 0.1 */
       double_MEMBER(classify_max_norm_scale_x, 0.325,
                     "Max char x-norm scale ...",
                     this->params()), /* PREV DEFAULT 0.3 */
       double_MEMBER(classify_min_norm_scale_y, 0.0, "Min char y-norm scale ...",
                     this->params()), /* PREV DEFAULT 0.1 */
       double_MEMBER(classify_max_norm_scale_y, 0.325,
                     "Max char y-norm scale ...",
                     this->params()), /* PREV DEFAULT 0.3 */
       double_MEMBER(classify_max_rating_ratio, 1.5,
                     "Veto ratio between classifier ratings", this->params()),
       double_MEMBER(classify_max_certainty_margin, 5.5,
                     "Veto difference between classifier certainties",
                     this->params()),
       BOOL_MEMBER(tess_cn_matching, 0, "Character Normalized Matching",
                   this->params()),
       BOOL_MEMBER(tess_bn_matching, 0, "Baseline Normalized Matching",
                   this->params()),
       BOOL_MEMBER(classify_enable_adaptive_matcher, 1,
                   "Enable adaptive classifier", this->params()),
       BOOL_MEMBER(classify_use_pre_adapted_templates, 0,
                   "Use pre-adapted classifier templates", this->params()),
       BOOL_MEMBER(classify_save_adapted_templates, 0,
                   "Save adapted templates to a file", this->params()),
       BOOL_MEMBER(classify_enable_adaptive_debugger, 0, "Enable match debugger",
                   this->params()),
       BOOL_MEMBER(classify_nonlinear_norm, 0,
                   "Non-linear stroke-density normalization", this->params()),
       INT_MEMBER(matcher_debug_level, 0, "Matcher Debug Level", this->params()),
       INT_MEMBER(matcher_debug_flags, 0, "Matcher Debug Flags", this->params()),
       INT_MEMBER(classify_learning_debug_level, 0, "Learning Debug Level: ",
                  this->params()),
       double_MEMBER(matcher_good_threshold, 0.125, "Good Match (0-1)",
                     this->params()),
       double_MEMBER(matcher_reliable_adaptive_result, 0.0, "Great Match (0-1)",
                     this->params()),
       double_MEMBER(matcher_perfect_threshold, 0.02, "Perfect Match (0-1)",
                     this->params()),
       double_MEMBER(matcher_bad_match_pad, 0.15, "Bad Match Pad (0-1)",
                     this->params()),
       double_MEMBER(matcher_rating_margin, 0.1, "New template margin (0-1)",
                     this->params()),
       double_MEMBER(matcher_avg_noise_size, 12.0, "Avg. noise blob length",
                     this->params()),
       INT_MEMBER(matcher_permanent_classes_min, 1, "Min # of permanent classes",
                  this->params()),
       INT_MEMBER(matcher_min_examples_for_prototyping, 3,
                  "Reliable Config Threshold", this->params()),
       INT_MEMBER(matcher_sufficient_examples_for_prototyping, 5,
                  "Enable adaption even if the ambiguities have not been seen",
                  this->params()),
       double_MEMBER(matcher_clustering_max_angle_delta, 0.015,
                     "Maximum angle delta for prototype clustering",
                     this->params()),
       double_MEMBER(classify_misfit_junk_penalty, 0.0,
                     "Penalty to apply when a non-alnum is vertically out of "
                     "its expected textline position",
                     this->params()),
       double_MEMBER(rating_scale, 1.5, "Rating scaling factor", this->params()),
       double_MEMBER(certainty_scale, 20.0, "Certainty scaling factor",
                     this->params()),
       double_MEMBER(tessedit_class_miss_scale, 0.00390625,
                     "Scale factor for features not used", this->params()),
       double_MEMBER(
           classify_adapted_pruning_factor, 2.5,
           "Prune poor adapted results this much worse than best result",
           this->params()),
       double_MEMBER(classify_adapted_pruning_threshold, -1.0,
                     "Threshold at which classify_adapted_pruning_factor starts",
                     this->params()),
       INT_MEMBER(classify_adapt_proto_threshold, 230,
                  "Threshold for good protos during adaptive 0-255",
                  this->params()),
       INT_MEMBER(classify_adapt_feature_threshold, 230,
                  "Threshold for good features during adaptive 0-255",
                  this->params()),
       BOOL_MEMBER(disable_character_fragments, TRUE,
                   "Do not include character fragments in the"
                   " results of the classifier",
                   this->params()),
       double_MEMBER(classify_character_fragments_garbage_certainty_threshold,
                     -3.0,
                     "Exclude fragments that do not look like whole"
                     " characters from training and adaption",
                     this->params()),
       BOOL_MEMBER(classify_debug_character_fragments, FALSE,
                   "Bring up graphical debugging windows for fragments training",
                   this->params()),
       BOOL_MEMBER(matcher_debug_separate_windows, FALSE,
                   "Use two different windows for debugging the matching: "
                   "One for the protos and one for the features.",
                   this->params()),
       STRING_MEMBER(classify_learn_debug_str, "", "Class str to debug learning",
                     this->params()),
       INT_MEMBER(classify_class_pruner_threshold, 229,
                  "Class Pruner Threshold 0-255", this->params()),
       INT_MEMBER(classify_class_pruner_multiplier, 15,
                  "Class Pruner Multiplier 0-255:       ", this->params()),
       INT_MEMBER(classify_cp_cutoff_strength, 7,
                  "Class Pruner CutoffStrength:         ", this->params()),
       INT_MEMBER(classify_integer_matcher_multiplier, 10,
                  "Integer Matcher Multiplier  0-255:   ", this->params()),
       EnableLearning(true),
       INT_MEMBER(il1_adaption_test, 0,
                  "Don't adapt to i/I at beginning of word", this->params()),
       BOOL_MEMBER(classify_bln_numeric_mode, 0,
                   "Assume the input is numbers [0-9].", this->params()),
       double_MEMBER(speckle_large_max_size, 0.30, "Max large speckle size",
                     this->params()),
       double_MEMBER(speckle_rating_penalty, 10.0,
                     "Penalty to add to worst rating for noise", this->params()),
       shape_table_(NULL),
       dict_(this),
       static_classifier_(NULL) {
   fontinfo_table_.set_compare_callback(
       NewPermanentTessCallback(CompareFontInfo));
   fontinfo_table_.set_clear_callback(
       NewPermanentTessCallback(FontInfoDeleteCallback));
   fontset_table_.set_compare_callback(
       NewPermanentTessCallback(CompareFontSet));
   fontset_table_.set_clear_callback(
       NewPermanentTessCallback(FontSetDeleteCallback));
   AdaptedTemplates = NULL;
   BackupAdaptedTemplates = NULL;
   PreTrainedTemplates = NULL;
   AllProtosOn = NULL;
   AllConfigsOn = NULL;
   AllConfigsOff = NULL;
   TempProtoMask = NULL;
   NormProtos = NULL;
 
   NumAdaptationsFailed = 0;
 
   learn_debug_win_ = NULL;
   learn_fragmented_word_debug_win_ = NULL;
   learn_fragments_debug_win_ = NULL;
 
   CharNormCutoffs = new uinT16[MAX_NUM_CLASSES];
   BaselineCutoffs = new uinT16[MAX_NUM_CLASSES];
 }

◆ ~Classify()

tesseract::Classify::~Classify ( )

virtual

Definition at line 192 of file classify.cpp.

                     {
   EndAdaptiveClassifier();
   delete learn_debug_win_;
   delete learn_fragmented_word_debug_win_;
   delete learn_fragments_debug_win_;
   delete[] CharNormCutoffs;
   delete[] BaselineCutoffs;
 }

Member Function Documentation

◆ AdaptableWord()

bool tesseract::Classify::AdaptableWord ( WERD_RES * word )

Return TRUE if the specified word is acceptable for adaptation.

Globals: none

Parameters

word	current word

Returns: TRUE or FALSE

Note: Exceptions: none; History: Thu May 30 14:25:06 1991, DSJ, Created.

Definition at line 850 of file adaptmatch.cpp.

                                            {
   if (word->best_choice == NULL) return false;
   int BestChoiceLength = word->best_choice->length();
   float adaptable_score =
     getDict().segment_penalty_dict_case_ok + ADAPTABLE_WERD_ADJUSTMENT;
   return   // rules that apply in general - simplest to compute first
       BestChoiceLength > 0 &&
       BestChoiceLength == word->rebuild_word->NumBlobs() &&
       BestChoiceLength <= MAX_ADAPTABLE_WERD_SIZE &&
       // This basically ensures that the word is at least a dictionary match
       // (freq word, user word, system dawg word, etc).
       // Since all the other adjustments will make adjust factor higher
       // than higher than adaptable_score=1.1+0.05=1.15
       // Since these are other flags that ensure that the word is dict word,
       // this check could be at times redundant.
       word->best_choice->adjust_factor() <= adaptable_score &&
       // Make sure that alternative choices are not dictionary words.
       word->AlternativeChoiceAdjustmentsWorseThan(adaptable_score);
 }

◆ AdaptiveClassifier()

void tesseract::Classify::AdaptiveClassifier	(	TBLOB *	Blob,
		BLOB_CHOICE_LIST *	Choices
	)

This routine calls the adaptive matcher which returns (in an array) the class id of each class matched.

It also returns the number of classes matched. For each class matched it places the best rating found for that class into the Ratings array.

Bad matches are then removed so that they don't need to be sorted. The remaining good matches are then sorted and converted to choices.

This routine also performs some simple speckle filtering.

Note: Exceptions: none; History: Mon Mar 11 10:00:58 1991, DSJ, Created.

Parameters

	Blob	blob to be classified
[out]	Choices	List of choices found by adaptive matcher. filled on return with the choices found by the class pruner and the ratings therefrom. Also contains the detailed results of the integer matcher.

Definition at line 185 of file adaptmatch.cpp.

                                                                         {
   assert(Choices != NULL);
   ADAPT_RESULTS *Results = new ADAPT_RESULTS;
   Results->Initialize();
 
   ASSERT_HOST(AdaptedTemplates != NULL);
 
   DoAdaptiveMatch(Blob, Results);
 
   RemoveBadMatches(Results);
   Results->match.sort(&UnicharRating::SortDescendingRating);
   RemoveExtraPuncs(Results);
   Results->ComputeBest();
   ConvertMatchesToChoices(Blob->denorm(), Blob->bounding_box(), Results,
                           Choices);
 
   // TODO(rays) Move to before ConvertMatchesToChoices!
   if (LargeSpeckle(*Blob) || Choices->length() == 0)
     AddLargeSpeckleTo(Results->BlobLength, Choices);
 
   if (matcher_debug_level >= 1) {
     tprintf("AD Matches =  ");
     PrintAdaptiveMatchResults(*Results);
   }
 
 #ifndef GRAPHICS_DISABLED
   if (classify_enable_adaptive_debugger)
     DebugAdaptiveClassifier(Blob, Results);
 #endif
 
   delete Results;
 }                                /* AdaptiveClassifier */

◆ AdaptiveClassifierIsEmpty()

bool tesseract::Classify::AdaptiveClassifierIsEmpty ( ) const

inline

Definition at line 285 of file classify.h.

                                          {
     return AdaptedTemplates->NumPermClasses == 0;
   }

◆ AdaptiveClassifierIsFull()

bool tesseract::Classify::AdaptiveClassifierIsFull ( ) const

inline

Definition at line 284 of file classify.h.

284 { return NumAdaptationsFailed > 0; }

◆ AdaptToChar()

void tesseract::Classify::AdaptToChar	(	TBLOB *	Blob,
		CLASS_ID	ClassId,
		int	FontinfoId,
		FLOAT32	Threshold,
		ADAPT_TEMPLATES	adaptive_templates
	)

Parameters

Blob	blob to add to templates for ClassId
ClassId	class to add blob to
FontinfoId	font information from pre-trained templates
Threshold	minimum match rating to existing template
adaptive_templates	current set of adapted templates

Globals:

AllProtosOn dummy mask to match against all protos
AllConfigsOn dummy mask to match against all configs

Returns: none

Note: Exceptions: none; History: Thu Mar 14 09:36:03 1991, DSJ, Created.

Definition at line 886 of file adaptmatch.cpp.

                                                                {
   int NumFeatures;
   INT_FEATURE_ARRAY IntFeatures;
   UnicharRating int_result;
   INT_CLASS IClass;
   ADAPT_CLASS Class;
   TEMP_CONFIG TempConfig;
   FEATURE_SET FloatFeatures;
   int NewTempConfigId;
 
   if (!LegalClassId (ClassId))
     return;
 
   int_result.unichar_id = ClassId;
   Class = adaptive_templates->Class[ClassId];
   assert(Class != NULL);
   if (IsEmptyAdaptedClass(Class)) {
     InitAdaptedClass(Blob, ClassId, FontinfoId, Class, adaptive_templates);
   } else {
     IClass = ClassForClassId(adaptive_templates->Templates, ClassId);
 
     NumFeatures = GetAdaptiveFeatures(Blob, IntFeatures, &FloatFeatures);
     if (NumFeatures <= 0) {
       return;  // Features already freed by GetAdaptiveFeatures.
     }
 
     // Only match configs with the matching font.
     BIT_VECTOR MatchingFontConfigs = NewBitVector(MAX_NUM_PROTOS);
     for (int cfg = 0; cfg < IClass->NumConfigs; ++cfg) {
       if (GetFontinfoId(Class, cfg) == FontinfoId) {
         SET_BIT(MatchingFontConfigs, cfg);
       } else {
         reset_bit(MatchingFontConfigs, cfg);
       }
     }
     im_.Match(IClass, AllProtosOn, MatchingFontConfigs,
               NumFeatures, IntFeatures,
               &int_result, classify_adapt_feature_threshold,
               NO_DEBUG, matcher_debug_separate_windows);
     FreeBitVector(MatchingFontConfigs);
 
     SetAdaptiveThreshold(Threshold);
 
     if (1.0f - int_result.rating <= Threshold) {
       if (ConfigIsPermanent(Class, int_result.config)) {
         if (classify_learning_debug_level >= 1)
           tprintf("Found good match to perm config %d = %4.1f%%.\n",
                   int_result.config, int_result.rating * 100.0);
         FreeFeatureSet(FloatFeatures);
         return;
       }
 
       TempConfig = TempConfigFor(Class, int_result.config);
       IncreaseConfidence(TempConfig);
       if (TempConfig->NumTimesSeen > Class->MaxNumTimesSeen) {
         Class->MaxNumTimesSeen = TempConfig->NumTimesSeen;
       }
       if (classify_learning_debug_level >= 1)
         tprintf("Increasing reliability of temp config %d to %d.\n",
                 int_result.config, TempConfig->NumTimesSeen);
 
       if (TempConfigReliable(ClassId, TempConfig)) {
         MakePermanent(adaptive_templates, ClassId, int_result.config, Blob);
         UpdateAmbigsGroup(ClassId, Blob);
       }
     } else {
       if (classify_learning_debug_level >= 1) {
         tprintf("Found poor match to temp config %d = %4.1f%%.\n",
                 int_result.config, int_result.rating * 100.0);
         if (classify_learning_debug_level > 2)
           DisplayAdaptedChar(Blob, IClass);
       }
       NewTempConfigId =
           MakeNewTemporaryConfig(adaptive_templates, ClassId, FontinfoId,
                                  NumFeatures, IntFeatures, FloatFeatures);
       if (NewTempConfigId >= 0 &&
           TempConfigReliable(ClassId, TempConfigFor(Class, NewTempConfigId))) {
         MakePermanent(adaptive_templates, ClassId, NewTempConfigId, Blob);
         UpdateAmbigsGroup(ClassId, Blob);
       }
 
 #ifndef GRAPHICS_DISABLED
       if (classify_learning_debug_level > 1) {
         DisplayAdaptedChar(Blob, IClass);
       }
 #endif
     }
     FreeFeatureSet(FloatFeatures);
   }
 }                                /* AdaptToChar */

◆ AddLargeSpeckleTo()

void tesseract::Classify::AddLargeSpeckleTo	(	int	blob_length,
		BLOB_CHOICE_LIST *	choices
	)

Definition at line 212 of file classify.cpp.

                                                                            {
     BLOB_CHOICE_IT bc_it(choices);
   // If there is no classifier result, we will use the worst possible certainty
   // and corresponding rating.
   float certainty = -getDict().certainty_scale;
   float rating = rating_scale * blob_length;
   if (!choices->empty() && blob_length > 0) {
     bc_it.move_to_last();
     BLOB_CHOICE* worst_choice = bc_it.data();
     // Add speckle_rating_penalty to worst rating, matching old value.
     rating = worst_choice->rating() + speckle_rating_penalty;
     // Compute the rating to correspond to the certainty. (Used to be kept
     // the same, but that messes up the language model search.)
     certainty = -rating * getDict().certainty_scale /
         (rating_scale * blob_length);
   }
   BLOB_CHOICE* blob_choice = new BLOB_CHOICE(UNICHAR_SPACE, rating, certainty,
                                              -1, 0.0f, MAX_FLOAT32, 0,
                                              BCC_SPECKLE_CLASSIFIER);
   bc_it.add_to_end(blob_choice);
 }

◆ AddNewResult()

void tesseract::Classify::AddNewResult	(	const UnicharRating &	new_result,
		ADAPT_RESULTS *	results
	)

This routine adds the result of a classification into Results. If the new rating is much worse than the current best rating, it is not entered into results because it would end up being stripped later anyway. If the new rating is better than the old rating for the class, it replaces the old rating. If this is the first rating for the class, the class is added to the list of matched classes in Results. If the new rating is better than the best so far, it becomes the best so far.

Globals:

matcher_bad_match_pad defines limits of an acceptable match

Parameters

	new_result	new result to add
[out]	results	results to add new result to

Note: Exceptions: none; History: Tue Mar 12 18:19:29 1991, DSJ, Created.

Definition at line 1030 of file adaptmatch.cpp.

                                                     {
   int old_match = FindScoredUnichar(new_result.unichar_id, *results);
 
   if (new_result.rating + matcher_bad_match_pad < results->best_rating ||
       (old_match < results->match.size() &&
        new_result.rating <= results->match[old_match].rating))
     return;  // New one not good enough.
 
   if (!unicharset.get_fragment(new_result.unichar_id))
     results->HasNonfragment = true;
 
   if (old_match < results->match.size()) {
     results->match[old_match].rating = new_result.rating;
   } else {
     results->match.push_back(new_result);
   }
 
   if (new_result.rating > results->best_rating &&
       // Ensure that fragments do not affect best rating, class and config.
       // This is needed so that at least one non-fragmented character is
       // always present in the results.
       // TODO(daria): verify that this helps accuracy and does not
       // hurt performance.
       !unicharset.get_fragment(new_result.unichar_id)) {
     results->best_match_index = old_match;
     results->best_rating = new_result.rating;
     results->best_unichar_id = new_result.unichar_id;
   }
 }                                /* AddNewResult */

◆ AmbigClassifier()

void tesseract::Classify::AmbigClassifier	(	const GenericVector< INT_FEATURE_STRUCT > &	int_features,
		const INT_FX_RESULT_STRUCT &	fx_info,
		const TBLOB *	blob,
		INT_TEMPLATES	templates,
		ADAPT_CLASS *	classes,
		UNICHAR_ID *	ambiguities,
		ADAPT_RESULTS *	results
	)

This routine is identical to CharNormClassifier() except that it does no class pruning. It simply matches the unknown blob against the classes listed in Ambiguities.

Globals:

AllProtosOn mask that enables all protos
AllConfigsOn mask that enables all configs

Parameters

	blob	blob to be classified
	templates	built-in templates to classify against
	classes	adapted class templates
	ambiguities	array of unichar id's to match against
[out]	results	place to put match results
	int_features
	fx_info

Note: Exceptions: none; History: Tue Mar 12 19:40:36 1991, DSJ, Created.

Definition at line 1084 of file adaptmatch.cpp.

                             {
   if (int_features.empty()) return;
   uinT8* CharNormArray = new uinT8[unicharset.size()];
   UnicharRating int_result;
 
   results->BlobLength = GetCharNormFeature(fx_info, templates, NULL,
                                            CharNormArray);
   bool debug = matcher_debug_level >= 2 || classify_debug_level > 1;
   if (debug)
     tprintf("AM Matches =  ");
 
   int top = blob->bounding_box().top();
   int bottom = blob->bounding_box().bottom();
   while (*ambiguities >= 0) {
     CLASS_ID class_id = *ambiguities;
 
     int_result.unichar_id = class_id;
     im_.Match(ClassForClassId(templates, class_id),
               AllProtosOn, AllConfigsOn,
               int_features.size(), &int_features[0],
               &int_result,
               classify_adapt_feature_threshold, NO_DEBUG,
               matcher_debug_separate_windows);
 
     ExpandShapesAndApplyCorrections(NULL, debug, class_id, bottom, top, 0,
                                     results->BlobLength,
                                     classify_integer_matcher_multiplier,
                                     CharNormArray, &int_result, results);
     ambiguities++;
   }
   delete [] CharNormArray;
 }                                /* AmbigClassifier */

◆ BaselineClassifier()

UNICHAR_ID * tesseract::Classify::BaselineClassifier	(	TBLOB *	Blob,
		const GenericVector< INT_FEATURE_STRUCT > &	int_features,
		const INT_FX_RESULT_STRUCT &	fx_info,
		ADAPT_TEMPLATES	Templates,
		ADAPT_RESULTS *	Results
	)

This routine extracts baseline normalized features from the unknown character and matches them against the specified set of templates. The classes which match are added to Results.

Globals:

BaselineCutoffs expected num features for each class

Parameters

Blob	blob to be classified
Templates	current set of adapted templates
Results	place to put match results
int_features
fx_info

Returns: Array of possible ambiguous chars that should be checked.

Note: Exceptions: none; History: Tue Mar 12 19:38:03 1991, DSJ, Created.

Definition at line 1306 of file adaptmatch.cpp.

                                                        {
   if (int_features.empty()) return NULL;
   uinT8* CharNormArray = new uinT8[unicharset.size()];
   ClearCharNormArray(CharNormArray);
 
   Results->BlobLength = IntCastRounded(fx_info.Length / kStandardFeatureLength);
   PruneClasses(Templates->Templates, int_features.size(), -1, &int_features[0],
                CharNormArray, BaselineCutoffs, &Results->CPResults);
 
   if (matcher_debug_level >= 2 || classify_debug_level > 1)
     tprintf("BL Matches =  ");
 
   MasterMatcher(Templates->Templates, int_features.size(), &int_features[0],
                 CharNormArray,
                 Templates->Class, matcher_debug_flags, 0,
                 Blob->bounding_box(), Results->CPResults, Results);
 
   delete [] CharNormArray;
   CLASS_ID ClassId = Results->best_unichar_id;
   if (ClassId == INVALID_UNICHAR_ID || Results->best_match_index < 0)
     return NULL;
 
   return Templates->Class[ClassId]->
       Config[Results->match[Results->best_match_index].config].Perm->Ambigs;
 }                                /* BaselineClassifier */

◆ CharNormClassifier()

int tesseract::Classify::CharNormClassifier	(	TBLOB *	blob,
		const TrainingSample &	sample,
		ADAPT_RESULTS *	adapt_results
	)

This routine extracts character normalized features from the unknown character and matches them against the specified set of templates. The classes which match are added to Results.

Parameters

blob	blob to be classified
sample	templates to classify unknown against
adapt_results	place to put match results

Globals:

CharNormCutoffs expected num features for each class
AllProtosOn mask that enables all protos
AllConfigsOn mask that enables all configs

Note: Exceptions: none; History: Tue Mar 12 16:02:52 1991, DSJ, Created.

Definition at line 1355 of file adaptmatch.cpp.

                                                                {
   // This is the length that is used for scaling ratings vs certainty.
   adapt_results->BlobLength =
       IntCastRounded(sample.outline_length() / kStandardFeatureLength);
   GenericVector<UnicharRating> unichar_results;
   static_classifier_->UnicharClassifySample(sample, blob->denorm().pix(), 0,
                                             -1, &unichar_results);
   // Convert results to the format used internally by AdaptiveClassifier.
   for (int r = 0; r < unichar_results.size(); ++r) {
     AddNewResult(unichar_results[r], adapt_results);
   }
   return sample.num_features();
 }                                /* CharNormClassifier */

◆ CharNormTrainingSample()

int tesseract::Classify::CharNormTrainingSample	(	bool	pruner_only,
		int	keep_this,
		const TrainingSample &	sample,
		GenericVector< UnicharRating > *	results
	)

Definition at line 1373 of file adaptmatch.cpp.

                                                                             {
   results->clear();
   ADAPT_RESULTS* adapt_results = new ADAPT_RESULTS();
   adapt_results->Initialize();
   // Compute the bounding box of the features.
   int num_features = sample.num_features();
   // Only the top and bottom of the blob_box are used by MasterMatcher, so
   // fabricate right and left using top and bottom.
   TBOX blob_box(sample.geo_feature(GeoBottom), sample.geo_feature(GeoBottom),
                 sample.geo_feature(GeoTop), sample.geo_feature(GeoTop));
   // Compute the char_norm_array from the saved cn_feature.
   FEATURE norm_feature = sample.GetCNFeature();
   uinT8* char_norm_array = new uinT8[unicharset.size()];
   int num_pruner_classes = MAX(unicharset.size(),
                                PreTrainedTemplates->NumClasses);
   uinT8* pruner_norm_array = new uinT8[num_pruner_classes];
   adapt_results->BlobLength =
       static_cast<int>(ActualOutlineLength(norm_feature) * 20 + 0.5);
   ComputeCharNormArrays(norm_feature, PreTrainedTemplates, char_norm_array,
                         pruner_norm_array);
 
   PruneClasses(PreTrainedTemplates, num_features, keep_this, sample.features(),
                pruner_norm_array,
                shape_table_ != NULL ? &shapetable_cutoffs_[0] : CharNormCutoffs,
                &adapt_results->CPResults);
   delete [] pruner_norm_array;
   if (keep_this >= 0) {
     adapt_results->CPResults[0].Class = keep_this;
     adapt_results->CPResults.truncate(1);
   }
   if (pruner_only) {
     // Convert pruner results to output format.
     for (int i = 0; i < adapt_results->CPResults.size(); ++i) {
       int class_id = adapt_results->CPResults[i].Class;
       results->push_back(
           UnicharRating(class_id, 1.0f - adapt_results->CPResults[i].Rating));
     }
   } else {
     MasterMatcher(PreTrainedTemplates, num_features, sample.features(),
                   char_norm_array,
                   NULL, matcher_debug_flags,
                   classify_integer_matcher_multiplier,
                   blob_box, adapt_results->CPResults, adapt_results);
     // Convert master matcher results to output format.
     for (int i = 0; i < adapt_results->match.size(); i++) {
       results->push_back(adapt_results->match[i]);
     }
     results->sort(&UnicharRating::SortDescendingRating);
   }
   delete [] char_norm_array;
   delete adapt_results;
   return num_features;
 }                                /* CharNormTrainingSample */

◆ ClassAndConfigIDToFontOrShapeID()

int tesseract::Classify::ClassAndConfigIDToFontOrShapeID	(	int	class_id,
		int	int_result_config
	)		const

Definition at line 2284 of file adaptmatch.cpp.

                                                                            {
   int font_set_id = PreTrainedTemplates->Class[class_id]->font_set_id;
   // Older inttemps have no font_ids.
   if (font_set_id < 0)
     return kBlankFontinfoId;
   const FontSet &fs = fontset_table_.get(font_set_id);
   ASSERT_HOST(int_result_config >= 0 && int_result_config < fs.size);
   return fs.configs[int_result_config];
 }

◆ ClassIDToDebugStr()

STRING tesseract::Classify::ClassIDToDebugStr	(	const INT_TEMPLATES_STRUCT *	templates,
		int	class_id,
		int	config_id
	)		const

Definition at line 2271 of file adaptmatch.cpp.

                                                                       {
   STRING class_string;
   if (templates == PreTrainedTemplates && shape_table_ != NULL) {
     int shape_id = ClassAndConfigIDToFontOrShapeID(class_id, config_id);
     class_string = shape_table_->DebugStr(shape_id);
   } else {
     class_string = unicharset.debug_str(class_id);
   }
   return class_string;
 }

◆ ClassifyAsNoise()

void tesseract::Classify::ClassifyAsNoise ( ADAPT_RESULTS * results )

This routine computes a rating which reflects the likelihood that the blob being classified is a noise blob. NOTE: assumes that the blob length has already been computed and placed into Results.

Parameters

results results to add noise classification to

Globals:

matcher_avg_noise_size avg. length of a noise blob

Note: Exceptions: none; History: Tue Mar 12 18:36:52 1991, DSJ, Created.

Definition at line 1446 of file adaptmatch.cpp.

                                                      {
   float rating = results->BlobLength / matcher_avg_noise_size;
   rating *= rating;
   rating /= 1.0 + rating;
 
   AddNewResult(UnicharRating(UNICHAR_SPACE, 1.0f - rating), results);
 }                                /* ClassifyAsNoise */

◆ ClearCharNormArray()

void tesseract::Classify::ClearCharNormArray ( uinT8 * char_norm_array )

For each class in the unicharset, clears the corresponding entry in char_norm_array. char_norm_array is indexed by unichar_id.

Globals:

none

Parameters

char_norm_array array to be cleared

Note: Exceptions: none; History: Wed Feb 20 11:20:54 1991, DSJ, Created.

Definition at line 48 of file float2int.cpp.

                                                         {
   memset(char_norm_array, 0, sizeof(*char_norm_array) * unicharset.size());
 }                                /* ClearCharNormArray */

◆ ComputeCharNormArrays()

void tesseract::Classify::ComputeCharNormArrays	(	FEATURE_STRUCT *	norm_feature,
		INT_TEMPLATES_STRUCT *	templates,
		uinT8 *	char_norm_array,
		uinT8 *	pruner_array
	)

Definition at line 1748 of file adaptmatch.cpp.

                                                           {
   ComputeIntCharNormArray(*norm_feature, char_norm_array);
   if (pruner_array != NULL) {
     if (shape_table_ == NULL) {
       ComputeIntCharNormArray(*norm_feature, pruner_array);
     } else {
       memset(pruner_array, MAX_UINT8,
              templates->NumClasses * sizeof(pruner_array[0]));
       // Each entry in the pruner norm array is the MIN of all the entries of
       // the corresponding unichars in the CharNormArray.
       for (int id = 0; id < templates->NumClasses; ++id) {
         int font_set_id = templates->Class[id]->font_set_id;
         const FontSet &fs = fontset_table_.get(font_set_id);
         for (int config = 0; config < fs.size; ++config) {
           const Shape& shape = shape_table_->GetShape(fs.configs[config]);
           for (int c = 0; c < shape.size(); ++c) {
             if (char_norm_array[shape[c].unichar_id] < pruner_array[id])
               pruner_array[id] = char_norm_array[shape[c].unichar_id];
           }
         }
       }
     }
   }
   FreeFeature(norm_feature);
 }

◆ ComputeCorrectedRating()

double tesseract::Classify::ComputeCorrectedRating	(	bool	debug,
		int	unichar_id,
		double	cp_rating,
		double	im_rating,
		int	feature_misses,
		int	bottom,
		int	top,
		int	blob_length,
		int	matcher_multiplier,
		const uinT8 *	cn_factors
	)

Definition at line 1241 of file adaptmatch.cpp.

                                                                  {
   // Compute class feature corrections.
   double cn_corrected = im_.ApplyCNCorrection(1.0 - im_rating, blob_length,
                                               cn_factors[unichar_id],
                                               matcher_multiplier);
   double miss_penalty = tessedit_class_miss_scale * feature_misses;
   double vertical_penalty = 0.0;
   // Penalize non-alnums for being vertical misfits.
   if (!unicharset.get_isalpha(unichar_id) &&
       !unicharset.get_isdigit(unichar_id) &&
       cn_factors[unichar_id] != 0 && classify_misfit_junk_penalty > 0.0) {
     int min_bottom, max_bottom, min_top, max_top;
     unicharset.get_top_bottom(unichar_id, &min_bottom, &max_bottom,
                               &min_top, &max_top);
     if (debug) {
       tprintf("top=%d, vs [%d, %d], bottom=%d, vs [%d, %d]\n",
               top, min_top, max_top, bottom, min_bottom, max_bottom);
     }
     if (top < min_top || top > max_top ||
         bottom < min_bottom || bottom > max_bottom) {
       vertical_penalty = classify_misfit_junk_penalty;
     }
   }
   double result = 1.0 - (cn_corrected + miss_penalty + vertical_penalty);
   if (result < WORST_POSSIBLE_RATING)
     result = WORST_POSSIBLE_RATING;
   if (debug) {
     tprintf("%s: %2.1f%%(CP%2.1f, IM%2.1f + CN%.2f(%d) + MP%2.1f + VP%2.1f)\n",
             unicharset.id_to_unichar(unichar_id),
             result * 100.0,
             cp_rating * 100.0,
             (1.0 - im_rating) * 100.0,
             (cn_corrected - (1.0 - im_rating)) * 100.0,
             cn_factors[unichar_id],
             miss_penalty * 100.0,
             vertical_penalty * 100.0);
   }
   return result;
 }

◆ ComputeIntCharNormArray()

void tesseract::Classify::ComputeIntCharNormArray	(	const FEATURE_STRUCT &	norm_feature,
		uinT8 *	char_norm_array
	)

For each class in unicharset, computes the match between norm_feature and the normalization protos for that class. Converts this number to the range from 0 - 255 and stores it into char_norm_array. CharNormArray is indexed by unichar_id.

Globals:

PreTrainedTemplates current set of built-in templates

Parameters

	norm_feature	character normalization feature
[out]	char_norm_array	place to put results of size unicharset.size()

Note: Exceptions: none; History: Wed Feb 20 11:20:54 1991, DSJ, Created.

Definition at line 69 of file float2int.cpp.

                                                                {
   for (int i = 0; i < unicharset.size(); i++) {
     if (i < PreTrainedTemplates->NumClasses) {
       int norm_adjust = static_cast<int>(INT_CHAR_NORM_RANGE *
         ComputeNormMatch(i, norm_feature, FALSE));
       char_norm_array[i] = ClipToRange(norm_adjust, 0, MAX_INT_CHAR_NORM);
     } else {
       // Classes with no templates (eg. ambigs & ligatures) default
       // to worst match.
       char_norm_array[i] = MAX_INT_CHAR_NORM;
     }
   }
 }                                /* ComputeIntCharNormArray */

◆ ComputeIntFeatures()

void tesseract::Classify::ComputeIntFeatures	(	FEATURE_SET	Features,
		INT_FEATURE_ARRAY	IntFeatures
	)

This routine converts each floating point pico-feature in Features into integer format and saves it into IntFeatures.

Globals:

none

Parameters

	Features	floating point pico-features to be converted
[out]	IntFeatures	array to put converted features into

Note: Exceptions: none; History: Wed Feb 20 10:58:45 1991, DSJ, Created.

Definition at line 100 of file float2int.cpp.

                                                                  {
   int Fid;
   FEATURE Feature;
   FLOAT32 YShift;
 
   if (classify_norm_method == baseline)
     YShift = BASELINE_Y_SHIFT;
   else
     YShift = Y_SHIFT;
 
   for (Fid = 0; Fid < Features->NumFeatures; Fid++) {
     Feature = Features->Features[Fid];
 
     IntFeatures[Fid].X =
         Bucket8For(Feature->Params[PicoFeatX], X_SHIFT, INT_FEAT_RANGE);
     IntFeatures[Fid].Y =
         Bucket8For(Feature->Params[PicoFeatY], YShift, INT_FEAT_RANGE);
     IntFeatures[Fid].Theta = CircBucketFor(Feature->Params[PicoFeatDir],
                                            ANGLE_SHIFT, INT_FEAT_RANGE);
     IntFeatures[Fid].CP_misses = 0;
   }
 }                                /* ComputeIntFeatures */

◆ ComputeNormMatch()

FLOAT32 tesseract::Classify::ComputeNormMatch	(	CLASS_ID	ClassId,
		const FEATURE_STRUCT &	feature,
		BOOL8	DebugMatch
	)

This routine compares Features against each character normalization proto for ClassId and returns the match rating of the best match.

Parameters

ClassId	id of class to match against
feature	character normalization feature
DebugMatch	controls dump of debug info

Globals: NormProtos character normalization prototypes

Returns: Best match rating for Feature against protos of ClassId.

Note: Exceptions: none; History: Wed Dec 19 16:56:12 1990, DSJ, Created.

Definition at line 88 of file normmatch.cpp.

                                                      {
   LIST Protos;
   FLOAT32 BestMatch;
   FLOAT32 Match;
   FLOAT32 Delta;
   PROTOTYPE *Proto;
   int ProtoId;
 
   if (ClassId >= NormProtos->NumProtos) {
     ClassId = NO_CLASS;
   }
 
   /* handle requests for classification as noise */
   if (ClassId == NO_CLASS) {
     /* kludge - clean up constants and make into control knobs later */
     Match = (feature.Params[CharNormLength] *
       feature.Params[CharNormLength] * 500.0 +
       feature.Params[CharNormRx] *
       feature.Params[CharNormRx] * 8000.0 +
       feature.Params[CharNormRy] *
       feature.Params[CharNormRy] * 8000.0);
     return (1.0 - NormEvidenceOf (Match));
   }
 
   BestMatch = MAX_FLOAT32;
   Protos = NormProtos->Protos[ClassId];
 
   if (DebugMatch) {
     tprintf("\nChar norm for class %s\n", unicharset.id_to_unichar(ClassId));
   }
 
   ProtoId = 0;
   iterate(Protos) {
     Proto = (PROTOTYPE *) first_node (Protos);
     Delta = feature.Params[CharNormY] - Proto->Mean[CharNormY];
     Match = Delta * Delta * Proto->Weight.Elliptical[CharNormY];
     if (DebugMatch) {
       tprintf("YMiddle: Proto=%g, Delta=%g, Var=%g, Dist=%g\n",
               Proto->Mean[CharNormY], Delta,
               Proto->Weight.Elliptical[CharNormY], Match);
     }
     Delta = feature.Params[CharNormRx] - Proto->Mean[CharNormRx];
     Match += Delta * Delta * Proto->Weight.Elliptical[CharNormRx];
     if (DebugMatch) {
       tprintf("Height: Proto=%g, Delta=%g, Var=%g, Dist=%g\n",
               Proto->Mean[CharNormRx], Delta,
               Proto->Weight.Elliptical[CharNormRx], Match);
     }
     // Ry is width! See intfx.cpp.
     Delta = feature.Params[CharNormRy] - Proto->Mean[CharNormRy];
     if (DebugMatch) {
       tprintf("Width: Proto=%g, Delta=%g, Var=%g\n",
               Proto->Mean[CharNormRy], Delta,
               Proto->Weight.Elliptical[CharNormRy]);
     }
     Delta = Delta * Delta * Proto->Weight.Elliptical[CharNormRy];
     Delta *= kWidthErrorWeighting;
     Match += Delta;
     if (DebugMatch) {
       tprintf("Total Dist=%g, scaled=%g, sigmoid=%g, penalty=%g\n",
               Match, Match / classify_norm_adj_midpoint,
               NormEvidenceOf(Match), 256 * (1 - NormEvidenceOf(Match)));
     }
 
     if (Match < BestMatch)
       BestMatch = Match;
 
     ProtoId++;
   }
   return 1.0 - NormEvidenceOf(BestMatch);
 }                                /* ComputeNormMatch */

◆ ConvertMatchesToChoices()

void tesseract::Classify::ConvertMatchesToChoices	(	const DENORM &	denorm,
		const TBOX &	box,
		ADAPT_RESULTS *	Results,
		BLOB_CHOICE_LIST *	Choices
	)

The function converts the given match ratings to the list of blob choices with ratings and certainties (used by the context checkers). If character fragments are present in the results, this function also makes sure that there is at least one non-fragmented classification included. For each classification result check the unicharset for "definite" ambiguities and modify the resulting Choices accordingly.

Definition at line 1460 of file adaptmatch.cpp.

                                                                   {
   assert(Choices != NULL);
   FLOAT32 Rating;
   FLOAT32 Certainty;
   BLOB_CHOICE_IT temp_it;
   bool contains_nonfrag = false;
   temp_it.set_to_list(Choices);
   int choices_length = 0;
   // With no shape_table_ maintain the previous MAX_MATCHES as the maximum
   // number of returned results, but with a shape_table_ we want to have room
   // for at least the biggest shape (which might contain hundreds of Indic
   // grapheme fragments) and more, so use double the size of the biggest shape
   // if that is more than the default.
   int max_matches = MAX_MATCHES;
   if (shape_table_ != NULL) {
     max_matches = shape_table_->MaxNumUnichars() * 2;
     if (max_matches < MAX_MATCHES)
       max_matches = MAX_MATCHES;
   }
 
   float best_certainty = -MAX_FLOAT32;
   for (int i = 0; i < Results->match.size(); i++) {
     const UnicharRating& result = Results->match[i];
     bool adapted = result.adapted;
     bool current_is_frag = (unicharset.get_fragment(result.unichar_id) != NULL);
     if (temp_it.length()+1 == max_matches &&
         !contains_nonfrag && current_is_frag) {
       continue;  // look for a non-fragmented character to fill the
                  // last spot in Choices if only fragments are present
     }
     // BlobLength can never be legally 0, this means recognition failed.
     // But we must return a classification result because some invoking
     // functions (chopper/permuter) do not anticipate a null blob choice.
     // So we need to assign a poor, but not infinitely bad score.
     if (Results->BlobLength == 0) {
       Certainty = -20;
       Rating = 100;    // should be -certainty * real_blob_length
     } else {
       Rating = Certainty = (1.0f - result.rating);
       Rating *= rating_scale * Results->BlobLength;
       Certainty *= -(getDict().certainty_scale);
     }
     // Adapted results, by their very nature, should have good certainty.
     // Those that don't are at best misleading, and often lead to errors,
     // so don't accept adapted results that are too far behind the best result,
     // whether adapted or static.
     // TODO(rays) find some way of automatically tuning these constants.
     if (Certainty > best_certainty) {
       best_certainty = MIN(Certainty, classify_adapted_pruning_threshold);
     } else if (adapted &&
                Certainty / classify_adapted_pruning_factor < best_certainty) {
       continue;  // Don't accept bad adapted results.
     }
 
     float min_xheight, max_xheight, yshift;
     denorm.XHeightRange(result.unichar_id, unicharset, box,
                         &min_xheight, &max_xheight, &yshift);
     BLOB_CHOICE* choice =
         new BLOB_CHOICE(result.unichar_id, Rating, Certainty,
                         unicharset.get_script(result.unichar_id),
                         min_xheight, max_xheight, yshift,
                         adapted ? BCC_ADAPTED_CLASSIFIER
                                 : BCC_STATIC_CLASSIFIER);
     choice->set_fonts(result.fonts);
     temp_it.add_to_end(choice);
     contains_nonfrag |= !current_is_frag;  // update contains_nonfrag
     choices_length++;
     if (choices_length >= max_matches) break;
   }
   Results->match.truncate(choices_length);
 }  // ConvertMatchesToChoices

◆ ConvertProto()

void tesseract::Classify::ConvertProto	(	PROTO	Proto,
		int	ProtoId,
		INT_CLASS	Class
	)

This routine converts Proto to integer format and installs it as ProtoId in Class.

Parameters

Proto	floating-pt proto to be converted to integer format
ProtoId	id of proto
Class	integer class to add converted proto to

Returns: none

Note: Globals: none; Exceptions: none; History: Fri Feb 8 11:22:43 1991, DSJ, Created.

Definition at line 516 of file intproto.cpp.

                                                                      {
   INT_PROTO P;
   FLOAT32 Param;
 
   assert(ProtoId < Class->NumProtos);
 
   P = ProtoForProtoId(Class, ProtoId);
 
   Param = Proto->A * 128;
   P->A = TruncateParam(Param, -128, 127, NULL);
 
   Param = -Proto->B * 256;
   P->B = TruncateParam(Param, 0, 255, NULL);
 
   Param = Proto->C * 128;
   P->C = TruncateParam(Param, -128, 127, NULL);
 
   Param = Proto->Angle * 256;
   if (Param < 0 || Param >= 256)
     P->Angle = 0;
   else
     P->Angle = (uinT8) Param;
 
   /* round proto length to nearest integer number of pico-features */
   Param = (Proto->Length / GetPicoFeatureLength()) + 0.5;
   Class->ProtoLengths[ProtoId] = TruncateParam(Param, 1, 255, NULL);
   if (classify_learning_debug_level >= 2)
     cprintf("Converted ffeat to (A=%d,B=%d,C=%d,L=%d)",
             P->A, P->B, P->C, Class->ProtoLengths[ProtoId]);
 }                                /* ConvertProto */

◆ CreateIntTemplates()

INT_TEMPLATES tesseract::Classify::CreateIntTemplates	(	CLASSES	FloatProtos,
		const UNICHARSET &	target_unicharset
	)

This routine converts from the old floating point format to the new integer format.

Parameters

FloatProtos	prototypes in old floating pt format
target_unicharset	the UNICHARSET to use

Returns: New set of training templates in integer format.

Note: Globals: none; Exceptions: none; History: Thu Feb 7 14:40:42 1991, DSJ, Created.

Definition at line 557 of file intproto.cpp.

                                                               {
   INT_TEMPLATES IntTemplates;
   CLASS_TYPE FClass;
   INT_CLASS IClass;
   int ClassId;
   int ProtoId;
   int ConfigId;
 
   IntTemplates = NewIntTemplates();
 
   for (ClassId = 0; ClassId < target_unicharset.size(); ClassId++) {
     FClass = &(FloatProtos[ClassId]);
     if (FClass->NumProtos == 0 && FClass->NumConfigs == 0 &&
         strcmp(target_unicharset.id_to_unichar(ClassId), " ") != 0) {
       cprintf("Warning: no protos/configs for %s in CreateIntTemplates()\n",
               target_unicharset.id_to_unichar(ClassId));
     }
     assert(UnusedClassIdIn(IntTemplates, ClassId));
     IClass = NewIntClass(FClass->NumProtos, FClass->NumConfigs);
     FontSet fs;
     fs.size = FClass->font_set.size();
     fs.configs = new int[fs.size];
     for (int i = 0; i < fs.size; ++i) {
       fs.configs[i] = FClass->font_set.get(i);
     }
     if (this->fontset_table_.contains(fs)) {
       IClass->font_set_id = this->fontset_table_.get_id(fs);
       delete[] fs.configs;
     } else {
       IClass->font_set_id = this->fontset_table_.push_back(fs);
     }
     AddIntClass(IntTemplates, ClassId, IClass);
 
     for (ProtoId = 0; ProtoId < FClass->NumProtos; ProtoId++) {
       AddIntProto(IClass);
       ConvertProto(ProtoIn(FClass, ProtoId), ProtoId, IClass);
       AddProtoToProtoPruner(ProtoIn(FClass, ProtoId), ProtoId, IClass,
                             classify_learning_debug_level >= 2);
       AddProtoToClassPruner(ProtoIn(FClass, ProtoId), ClassId, IntTemplates);
     }
 
     for (ConfigId = 0; ConfigId < FClass->NumConfigs; ConfigId++) {
       AddIntConfig(IClass);
       ConvertConfig(FClass->Configurations[ConfigId], ConfigId, IClass);
     }
   }
   return (IntTemplates);
 }                                /* CreateIntTemplates */

◆ DebugAdaptiveClassifier()

void tesseract::Classify::DebugAdaptiveClassifier	(	TBLOB *	blob,
		ADAPT_RESULTS *	Results
	)

Parameters

blob	blob whose classification is being debugged
Results	results of match being debugged

Globals: none

Note: Exceptions: none; History: Wed Mar 13 16:44:41 1991, DSJ, Created.

Definition at line 1547 of file adaptmatch.cpp.

                                                                {
   if (static_classifier_ == NULL) return;
   INT_FX_RESULT_STRUCT fx_info;
   GenericVector<INT_FEATURE_STRUCT> bl_features;
   TrainingSample* sample =
       BlobToTrainingSample(*blob, false, &fx_info, &bl_features);
   if (sample == NULL) return;
   static_classifier_->DebugDisplay(*sample, blob->denorm().pix(),
                                    Results->best_unichar_id);
 }                                /* DebugAdaptiveClassifier */

◆ DisplayAdaptedChar()

void tesseract::Classify::DisplayAdaptedChar	(	TBLOB *	blob,
		INT_CLASS_STRUCT *	int_class
	)

Definition at line 979 of file adaptmatch.cpp.

                                                                           {
 #ifndef GRAPHICS_DISABLED
   INT_FX_RESULT_STRUCT fx_info;
   GenericVector<INT_FEATURE_STRUCT> bl_features;
   TrainingSample* sample =
       BlobToTrainingSample(*blob, classify_nonlinear_norm, &fx_info,
                            &bl_features);
   if (sample == NULL) return;
 
   UnicharRating int_result;
   im_.Match(int_class, AllProtosOn, AllConfigsOn,
             bl_features.size(), &bl_features[0],
             &int_result, classify_adapt_feature_threshold,
             NO_DEBUG, matcher_debug_separate_windows);
   tprintf("Best match to temp config %d = %4.1f%%.\n",
           int_result.config, int_result.rating * 100.0);
   if (classify_learning_debug_level >= 2) {
     uinT32 ConfigMask;
     ConfigMask = 1 << int_result.config;
     ShowMatchDisplay();
     im_.Match(int_class, AllProtosOn, (BIT_VECTOR)&ConfigMask,
               bl_features.size(), &bl_features[0],
               &int_result, classify_adapt_feature_threshold,
               6 | 0x19, matcher_debug_separate_windows);
     UpdateMatchDisplay();
   }
 
   delete sample;
 #endif
 }

◆ DoAdaptiveMatch()

void tesseract::Classify::DoAdaptiveMatch	(	TBLOB *	Blob,
		ADAPT_RESULTS *	Results
	)

This routine performs an adaptive classification. If we have not yet adapted to enough classes, a simple classification to the pre-trained templates is performed. Otherwise, we match the blob against the adapted templates. If the adapted templates do not match well, we try a match against the pre-trained templates. If an adapted template match is found, we do a match to any pre-trained templates which could be ambiguous. The results from all of these classifications are merged together into Results.

Parameters

Blob	blob to be classified
Results	place to put match results

Globals:

PreTrainedTemplates built-in training templates
AdaptedTemplates templates adapted for this page
matcher_reliable_adaptive_result rating limit for a great match

Note: Exceptions: none; History: Tue Mar 12 08:50:11 1991, DSJ, Created.

Definition at line 1583 of file adaptmatch.cpp.

                                                                   {
   UNICHAR_ID *Ambiguities;
 
   INT_FX_RESULT_STRUCT fx_info;
   GenericVector<INT_FEATURE_STRUCT> bl_features;
   TrainingSample* sample =
       BlobToTrainingSample(*Blob, classify_nonlinear_norm, &fx_info,
                            &bl_features);
   if (sample == NULL) return;
 
   if (AdaptedTemplates->NumPermClasses < matcher_permanent_classes_min ||
       tess_cn_matching) {
     CharNormClassifier(Blob, *sample, Results);
   } else {
     Ambiguities = BaselineClassifier(Blob, bl_features, fx_info,
                                      AdaptedTemplates, Results);
     if ((!Results->match.empty() &&
          MarginalMatch(Results->best_rating,
                        matcher_reliable_adaptive_result) &&
          !tess_bn_matching) ||
         Results->match.empty()) {
       CharNormClassifier(Blob, *sample, Results);
     } else if (Ambiguities && *Ambiguities >= 0 && !tess_bn_matching) {
       AmbigClassifier(bl_features, fx_info, Blob,
                       PreTrainedTemplates,
                       AdaptedTemplates->Class,
                       Ambiguities,
                       Results);
     }
   }
 
   // Force the blob to be classified as noise
   // if the results contain only fragments.
   // TODO(daria): verify that this is better than
   // just adding a NULL classification.
   if (!Results->HasNonfragment || Results->match.empty())
     ClassifyAsNoise(Results);
   delete sample;
 }   /* DoAdaptiveMatch */

◆ EndAdaptiveClassifier()

void tesseract::Classify::EndAdaptiveClassifier ( )

This routine performs cleanup operations on the adaptive classifier. It should be called before the program is terminated. Its main function is to save the adapted templates to a file.

Globals:

AdaptedTemplates current set of adapted templates
classify_save_adapted_templates TRUE if templates should be saved
classify_enable_adaptive_matcher TRUE if adaptive matcher is enabled

Note: Exceptions: none; History: Tue Mar 19 14:37:06 1991, DSJ, Created.

Definition at line 456 of file adaptmatch.cpp.

                                      {
   STRING Filename;
   FILE *File;
 
   if (AdaptedTemplates != NULL &&
       classify_enable_adaptive_matcher && classify_save_adapted_templates) {
     Filename = imagefile + ADAPT_TEMPLATE_SUFFIX;
     File = fopen (Filename.string(), "wb");
     if (File == NULL)
       cprintf ("Unable to save adapted templates to %s!\n", Filename.string());
     else {
       cprintf ("\nSaving adapted templates to %s ...", Filename.string());
       fflush(stdout);
       WriteAdaptedTemplates(File, AdaptedTemplates);
       cprintf ("\n");
       fclose(File);
     }
   }
 
   if (AdaptedTemplates != NULL) {
     free_adapted_templates(AdaptedTemplates);
     AdaptedTemplates = NULL;
   }
   if (BackupAdaptedTemplates != NULL) {
     free_adapted_templates(BackupAdaptedTemplates);
     BackupAdaptedTemplates = NULL;
   }
 
   if (PreTrainedTemplates != NULL) {
     free_int_templates(PreTrainedTemplates);
     PreTrainedTemplates = NULL;
   }
   getDict().EndDangerousAmbigs();
   FreeNormProtos();
   if (AllProtosOn != NULL) {
     FreeBitVector(AllProtosOn);
     FreeBitVector(AllConfigsOn);
     FreeBitVector(AllConfigsOff);
     FreeBitVector(TempProtoMask);
     AllProtosOn = NULL;
     AllConfigsOn = NULL;
     AllConfigsOff = NULL;
     TempProtoMask = NULL;
   }
   delete shape_table_;
   shape_table_ = NULL;
   if (static_classifier_ != NULL) {
     delete static_classifier_;
     static_classifier_ = NULL;
   }
 }                                /* EndAdaptiveClassifier */

◆ ExpandShapesAndApplyCorrections()

void tesseract::Classify::ExpandShapesAndApplyCorrections	(	ADAPT_CLASS *	classes,
		bool	debug,
		int	class_id,
		int	bottom,
		int	top,
		float	cp_rating,
		int	blob_length,
		int	matcher_multiplier,
		const uinT8 *	cn_factors,
		UnicharRating *	int_result,
		ADAPT_RESULTS *	final_results
	)

Definition at line 1167 of file adaptmatch.cpp.

                                                              {
   if (classes != NULL) {
     // Adapted result. Convert configs to fontinfo_ids.
     int_result->adapted = true;
     for (int f = 0; f < int_result->fonts.size(); ++f) {
       int_result->fonts[f].fontinfo_id =
           GetFontinfoId(classes[class_id], int_result->fonts[f].fontinfo_id);
     }
   } else {
     // Pre-trained result. Map fonts using font_sets_.
     int_result->adapted = false;
     for (int f = 0; f < int_result->fonts.size(); ++f) {
       int_result->fonts[f].fontinfo_id =
           ClassAndConfigIDToFontOrShapeID(class_id,
                                           int_result->fonts[f].fontinfo_id);
     }
     if (shape_table_ != NULL) {
       // Two possible cases:
       // 1. Flat shapetable. All unichar-ids of the shapes referenced by
       // int_result->fonts are the same. In this case build a new vector of
       // mapped fonts and replace the fonts in int_result.
       // 2. Multi-unichar shapetable. Variable unichars in the shapes referenced
       // by int_result. In this case, build a vector of UnicharRating to
       // gather together different font-ids for each unichar. Also covers case1.
       GenericVector<UnicharRating> mapped_results;
       for (int f = 0; f < int_result->fonts.size(); ++f) {
         int shape_id = int_result->fonts[f].fontinfo_id;
         const Shape& shape = shape_table_->GetShape(shape_id);
         for (int c = 0; c < shape.size(); ++c) {
           int unichar_id = shape[c].unichar_id;
           if (!unicharset.get_enabled(unichar_id)) continue;
           // Find the mapped_result for unichar_id.
           int r = 0;
           for (r = 0; r < mapped_results.size() &&
                mapped_results[r].unichar_id != unichar_id; ++r) {}
           if (r == mapped_results.size()) {
             mapped_results.push_back(*int_result);
             mapped_results[r].unichar_id = unichar_id;
             mapped_results[r].fonts.truncate(0);
           }
           for (int i = 0; i < shape[c].font_ids.size(); ++i) {
             mapped_results[r].fonts.push_back(
                 ScoredFont(shape[c].font_ids[i], int_result->fonts[f].score));
           }
         }
       }
       for (int m = 0; m < mapped_results.size(); ++m) {
         mapped_results[m].rating =
             ComputeCorrectedRating(debug, mapped_results[m].unichar_id,
                                    cp_rating, int_result->rating,
                                    int_result->feature_misses, bottom, top,
                                    blob_length, matcher_multiplier, cn_factors);
         AddNewResult(mapped_results[m], final_results);
       }
       return;
     }
   }
   if (unicharset.get_enabled(class_id)) {
     int_result->rating = ComputeCorrectedRating(debug, class_id, cp_rating,
                                                 int_result->rating,
                                                 int_result->feature_misses,
                                                 bottom, top, blob_length,
                                                 matcher_multiplier, cn_factors);
     AddNewResult(*int_result, final_results);
   }
 }

◆ ExtractFeatures()

void tesseract::Classify::ExtractFeatures	(	const TBLOB &	blob,
		bool	nonlinear_norm,
		GenericVector< INT_FEATURE_STRUCT > *	bl_features,
		GenericVector< INT_FEATURE_STRUCT > *	cn_features,
		INT_FX_RESULT_STRUCT *	results,
		GenericVector< int > *	outline_cn_counts
	)

static

Definition at line 445 of file intfx.cpp.

                                                                       {
   DENORM bl_denorm, cn_denorm;
   tesseract::Classify::SetupBLCNDenorms(blob, nonlinear_norm,
                                         &bl_denorm, &cn_denorm, results);
   if (outline_cn_counts != NULL)
     outline_cn_counts->truncate(0);
   // Iterate the outlines.
   for (TESSLINE* ol = blob.outlines; ol != NULL; ol = ol->next) {
     // Iterate the polygon.
     EDGEPT* loop_pt = ol->FindBestStartPt();
     EDGEPT* pt = loop_pt;
     if (pt == NULL) continue;
     do {
       if (pt->IsHidden()) continue;
       // Find a run of equal src_outline.
       EDGEPT* last_pt = pt;
       do {
         last_pt = last_pt->next;
       } while (last_pt != loop_pt && !last_pt->IsHidden() &&
                last_pt->src_outline == pt->src_outline);
       last_pt = last_pt->prev;
       // Until the adaptive classifier can be weaned off polygon segments,
       // we have to force extraction from the polygon for the bl_features.
       ExtractFeaturesFromRun(pt, last_pt, bl_denorm, kStandardFeatureLength,
                              true, bl_features);
       ExtractFeaturesFromRun(pt, last_pt, cn_denorm, kStandardFeatureLength,
                              false, cn_features);
       pt = last_pt;
     } while ((pt = pt->next) != loop_pt);
     if (outline_cn_counts != NULL)
       outline_cn_counts->push_back(cn_features->size());
   }
   results->NumBL = bl_features->size();
   results->NumCN = cn_features->size();
   results->YBottom = blob.bounding_box().bottom();
   results->YTop = blob.bounding_box().top();
   results->Width = blob.bounding_box().width();
 }

◆ ExtractIntCNFeatures()

FEATURE_SET tesseract::Classify::ExtractIntCNFeatures	(	const TBLOB &	blob,
		const INT_FX_RESULT_STRUCT &	fx_info
	)

Parameters

blob	blob to extract features from
fx_info

Returns: Integer character-normalized features for blob.

Note: Exceptions: none; History: 8/8/2011, rays, Created.

Definition at line 230 of file picofeat.cpp.

                                                             {
   INT_FX_RESULT_STRUCT local_fx_info(fx_info);
   GenericVector<INT_FEATURE_STRUCT> bl_features;
   tesseract::TrainingSample* sample = tesseract::BlobToTrainingSample(
       blob, false, &local_fx_info, &bl_features);
   if (sample == NULL) return NULL;
 
   int num_features = sample->num_features();
   const INT_FEATURE_STRUCT* features = sample->features();
   FEATURE_SET feature_set = NewFeatureSet(num_features);
   for (int f = 0; f < num_features; ++f) {
     FEATURE feature = NewFeature(&IntFeatDesc);
 
     feature->Params[IntX] = features[f].X;
     feature->Params[IntY] = features[f].Y;
     feature->Params[IntDir] = features[f].Theta;
     AddFeature(feature_set, feature);
   }
   delete sample;
 
   return feature_set;
 }                                /* ExtractIntCNFeatures */

◆ ExtractIntGeoFeatures()

FEATURE_SET tesseract::Classify::ExtractIntGeoFeatures	(	const TBLOB &	blob,
		const INT_FX_RESULT_STRUCT &	fx_info
	)

Parameters

blob	blob to extract features from
fx_info

Returns: Geometric (top/bottom/width) features for blob.

Note: Exceptions: none; History: 8/8/2011, rays, Created.

Definition at line 262 of file picofeat.cpp.

                                                             {
   INT_FX_RESULT_STRUCT local_fx_info(fx_info);
   GenericVector<INT_FEATURE_STRUCT> bl_features;
   tesseract::TrainingSample* sample = tesseract::BlobToTrainingSample(
       blob, false, &local_fx_info, &bl_features);
   if (sample == NULL) return NULL;
 
   FEATURE_SET feature_set = NewFeatureSet(1);
   FEATURE feature = NewFeature(&IntFeatDesc);
 
   feature->Params[GeoBottom] = sample->geo_feature(GeoBottom);
   feature->Params[GeoTop] = sample->geo_feature(GeoTop);
   feature->Params[GeoWidth] = sample->geo_feature(GeoWidth);
   AddFeature(feature_set, feature);
   delete sample;
 
   return feature_set;
 }                                /* ExtractIntGeoFeatures */

◆ ExtractOutlineFeatures()

FEATURE_SET tesseract::Classify::ExtractOutlineFeatures ( TBLOB * Blob )

Convert each segment in the outline to a feature and return the features.

Parameters

Blob	blob to extract pico-features from

Returns: Outline-features for Blob.

Note

Globals: none

Exceptions: none

History:

11/13/90, DSJ, Created.
05/24/91, DSJ, Updated for either char or baseline normalize.

Definition at line 47 of file outfeat.cpp.

                                                         {
   LIST Outlines;
   LIST RemainingOutlines;
   MFOUTLINE Outline;
   FEATURE_SET FeatureSet;
   FLOAT32 XScale, YScale;
 
   FeatureSet = NewFeatureSet (MAX_OUTLINE_FEATURES);
   if (Blob == NULL)
     return (FeatureSet);
 
   Outlines = ConvertBlob (Blob);
 
   NormalizeOutlines(Outlines, &XScale, &YScale);
   RemainingOutlines = Outlines;
   iterate(RemainingOutlines) {
     Outline = (MFOUTLINE) first_node (RemainingOutlines);
     ConvertToOutlineFeatures(Outline, FeatureSet);
   }
   if (classify_norm_method == baseline)
     NormalizeOutlineX(FeatureSet);
   FreeOutlines(Outlines);
   return (FeatureSet);
 }                                /* ExtractOutlineFeatures */

◆ ExtractPicoFeatures()

FEATURE_SET tesseract::Classify::ExtractPicoFeatures ( TBLOB * Blob )

Operation: Dummy for now.

Globals:

classify_norm_method normalization method currently specified
Parameters

Blob blob to extract pico-features from

Returns
Pico-features for Blob.

Note
Exceptions: none

History: 9/4/90, DSJ, Created.

Definition at line 67 of file picofeat.cpp.

                                                      {
   LIST Outlines;
   LIST RemainingOutlines;
   MFOUTLINE Outline;
   FEATURE_SET FeatureSet;
   FLOAT32 XScale, YScale;
 
   FeatureSet = NewFeatureSet(MAX_PICO_FEATURES);
   Outlines = ConvertBlob(Blob);
   NormalizeOutlines(Outlines, &XScale, &YScale);
   RemainingOutlines = Outlines;
   iterate(RemainingOutlines) {
     Outline = (MFOUTLINE) first_node (RemainingOutlines);
     ConvertToPicoFeatures2(Outline, FeatureSet);
   }
   if (classify_norm_method == baseline)
     NormalizePicoX(FeatureSet);
   FreeOutlines(Outlines);
   return (FeatureSet);
 
 }                                /* ExtractPicoFeatures */

◆ FreeNormProtos()

void tesseract::Classify::FreeNormProtos ( )

Definition at line 162 of file normmatch.cpp.

                               {
   if (NormProtos != NULL) {
     for (int i = 0; i < NormProtos->NumProtos; i++)
       FreeProtoList(&NormProtos->Protos[i]);
     Efree(NormProtos->Protos);
     Efree(NormProtos->ParamDesc);
     Efree(NormProtos);
     NormProtos = NULL;
   }
 }

◆ get_fontinfo_table() [1/2]

UnicityTable<FontInfo>& tesseract::Classify::get_fontinfo_table ( )

inline

Definition at line 345 of file classify.h.

                                                {
     return fontinfo_table_;
   }

◆ get_fontinfo_table() [2/2]

const UnicityTable<FontInfo>& tesseract::Classify::get_fontinfo_table ( ) const

inline

Definition at line 348 of file classify.h.

                                                            {
     return fontinfo_table_;
   }

◆ get_fontset_table()

UnicityTable<FontSet>& tesseract::Classify::get_fontset_table ( )

inline

Definition at line 351 of file classify.h.

                                              {
     return fontset_table_;
   }

◆ GetAdaptiveFeatures()

int tesseract::Classify::GetAdaptiveFeatures	(	TBLOB *	Blob,
		INT_FEATURE_ARRAY	IntFeatures,
		FEATURE_SET *	FloatFeatures
	)

This routine sets up the feature extractor to extract baseline normalized pico-features.

The extracted pico-features are converted to integer form and placed in IntFeatures. The original floating-pt. features are returned in FloatFeatures.

Globals: none

Parameters

	Blob	blob to extract features from
[out]	IntFeatures	array to fill with integer features
[out]	FloatFeatures	place to return actual floating-pt features

Returns: Number of pico-features returned (0 if an error occurred)

Note: Exceptions: none; History: Tue Mar 12 17:55:18 1991, DSJ, Created.

Definition at line 812 of file adaptmatch.cpp.

                                                               {
   FEATURE_SET Features;
   int NumFeatures;
 
   classify_norm_method.set_value(baseline);
   Features = ExtractPicoFeatures(Blob);
 
   NumFeatures = Features->NumFeatures;
   if (NumFeatures == 0 || NumFeatures > UNLIKELY_NUM_FEAT) {
     FreeFeatureSet(Features);
     return 0;
   }
 
   ComputeIntFeatures(Features, IntFeatures);
   *FloatFeatures = Features;
 
   return NumFeatures;
 }                                /* GetAdaptiveFeatures */

◆ GetAmbiguities()

UNICHAR_ID * tesseract::Classify::GetAmbiguities	(	TBLOB *	Blob,
		CLASS_ID	CorrectClass
	)

This routine matches blob to the built-in templates to find out if there are any classes other than the correct class which are potential ambiguities.

Parameters

Blob	blob to get classification ambiguities for
CorrectClass	correct class for Blob

Globals:

CurrentRatings used by qsort compare routine
PreTrainedTemplates built-in templates

Returns: String containing all possible ambiguous classes.

Note: Exceptions: none; History: Fri Mar 15 08:08:22 1991, DSJ, Created.

Definition at line 1640 of file adaptmatch.cpp.

                                                             {
   ADAPT_RESULTS *Results = new ADAPT_RESULTS();
   UNICHAR_ID *Ambiguities;
   int i;
 
   Results->Initialize();
   INT_FX_RESULT_STRUCT fx_info;
   GenericVector<INT_FEATURE_STRUCT> bl_features;
   TrainingSample* sample =
       BlobToTrainingSample(*Blob, classify_nonlinear_norm, &fx_info,
                            &bl_features);
   if (sample == NULL) {
     delete Results;
     return NULL;
   }
 
   CharNormClassifier(Blob, *sample, Results);
   delete sample;
   RemoveBadMatches(Results);
   Results->match.sort(&UnicharRating::SortDescendingRating);
 
   /* copy the class id's into an string of ambiguities - don't copy if
      the correct class is the only class id matched */
   Ambiguities = new UNICHAR_ID[Results->match.size() + 1];
   if (Results->match.size() > 1 ||
       (Results->match.size() == 1 &&
           Results->match[0].unichar_id != CorrectClass)) {
     for (i = 0; i < Results->match.size(); i++)
       Ambiguities[i] = Results->match[i].unichar_id;
     Ambiguities[i] = -1;
   } else {
     Ambiguities[0] = -1;
   }
 
   delete Results;
   return Ambiguities;
 }                              /* GetAmbiguities */

◆ GetCharNormFeature()

int tesseract::Classify::GetCharNormFeature	(	const INT_FX_RESULT_STRUCT &	fx_info,
		INT_TEMPLATES	templates,
		uinT8 *	pruner_norm_array,
		uinT8 *	char_norm_array
	)

This routine calls the integer (Hardware) feature extractor if it has not been called before for this blob.

The results from the feature extractor are placed into globals so that they can be used in other routines without re-extracting the features.

It then copies the char norm features into the IntFeatures array provided by the caller.

Parameters

templates	used to compute char norm adjustments
pruner_norm_array	Array of factors from blob normalization process
char_norm_array	array to fill with dummy char norm adjustments
fx_info	Globals:

Returns: Number of features extracted or 0 if an error occurred.

Note: Exceptions: none; History: Tue May 28 10:40:52 1991, DSJ, Created.

Definition at line 1728 of file adaptmatch.cpp.

                                                          {
   FEATURE norm_feature = NewFeature(&CharNormDesc);
   float baseline = kBlnBaselineOffset;
   float scale = MF_SCALE_FACTOR;
   norm_feature->Params[CharNormY] = (fx_info.Ymean - baseline) * scale;
   norm_feature->Params[CharNormLength] =
       fx_info.Length * scale / LENGTH_COMPRESSION;
   norm_feature->Params[CharNormRx] = fx_info.Rx * scale;
   norm_feature->Params[CharNormRy] = fx_info.Ry * scale;
   // Deletes norm_feature.
   ComputeCharNormArrays(norm_feature, templates, char_norm_array,
                         pruner_norm_array);
   return IntCastRounded(fx_info.Length / kStandardFeatureLength);
 }                              /* GetCharNormFeature */

◆ GetClassToDebug()

CLASS_ID tesseract::Classify::GetClassToDebug	(	const char *	Prompt,
		bool *	adaptive_on,
		bool *	pretrained_on,
		int *	shape_id
	)

This routine prompts the user with Prompt and waits for the user to enter something in the debug window.

Parameters

Prompt	prompt to print while waiting for input from window
adaptive_on
pretrained_on
shape_id

Returns: Character entered in the debug window.

Note: Globals: none; Exceptions: none; History: Thu Mar 21 16:55:13 1991, DSJ, Created.

Definition at line 1391 of file intproto.cpp.

                                                                        {
   tprintf("%s\n", Prompt);
   SVEvent* ev;
   SVEventType ev_type;
   int unichar_id = INVALID_UNICHAR_ID;
   // Wait until a click or popup event.
   do {
     ev = IntMatchWindow->AwaitEvent(SVET_ANY);
     ev_type = ev->type;
     if (ev_type == SVET_POPUP) {
       if (ev->command_id == IDA_SHAPE_INDEX) {
         if (shape_table_ != NULL) {
           *shape_id = atoi(ev->parameter);
           *adaptive_on = false;
           *pretrained_on = true;
           if (*shape_id >= 0 && *shape_id < shape_table_->NumShapes()) {
             int font_id;
             shape_table_->GetFirstUnicharAndFont(*shape_id, &unichar_id,
                                                  &font_id);
             tprintf("Shape %d, first unichar=%d, font=%d\n",
                     *shape_id, unichar_id, font_id);
             return unichar_id;
           }
           tprintf("Shape index '%s' not found in shape table\n", ev->parameter);
         } else {
           tprintf("No shape table loaded!\n");
         }
       } else {
         if (unicharset.contains_unichar(ev->parameter)) {
           unichar_id = unicharset.unichar_to_id(ev->parameter);
           if (ev->command_id == IDA_ADAPTIVE) {
             *adaptive_on = true;
             *pretrained_on = false;
             *shape_id = -1;
           } else if (ev->command_id == IDA_STATIC) {
             *adaptive_on = false;
             *pretrained_on = true;
           } else {
             *adaptive_on = true;
             *pretrained_on = true;
           }
           if (ev->command_id == IDA_ADAPTIVE || shape_table_ == NULL) {
             *shape_id = -1;
             return unichar_id;
           }
           for (int s = 0; s < shape_table_->NumShapes(); ++s) {
             if (shape_table_->GetShape(s).ContainsUnichar(unichar_id)) {
               tprintf("%s\n", shape_table_->DebugStr(s).string());
             }
           }
         } else {
           tprintf("Char class '%s' not found in unicharset",
                   ev->parameter);
         }
       }
     }
     delete ev;
   } while (ev_type != SVET_CLICK);
   return 0;
 }                                /* GetClassToDebug */

◆ getDict()

Dict& tesseract::Classify::getDict ( )

inline

Definition at line 65 of file classify.h.

                   {
     return dict_;
   }

◆ GetFontinfoId()

int tesseract::Classify::GetFontinfoId	(	ADAPT_CLASS	Class,
		uinT8	ConfigId
	)

Definition at line 190 of file adaptive.cpp.

                                                              {
   return (ConfigIsPermanent(Class, ConfigId) ?
       PermConfigFor(Class, ConfigId)->FontinfoId :
       TempConfigFor(Class, ConfigId)->FontinfoId);
 }

◆ InitAdaptedClass()

void tesseract::Classify::InitAdaptedClass	(	TBLOB *	Blob,
		CLASS_ID	ClassId,
		int	FontinfoId,
		ADAPT_CLASS	Class,
		ADAPT_TEMPLATES	Templates
	)

This routine creates a new adapted class and uses Blob as the model for the first config in that class.

Parameters

Blob	blob to model new class after
ClassId	id of the class to be initialized
FontinfoId	font information inferred from pre-trained templates
Class	adapted class to be initialized
Templates	adapted templates to add new class to

Globals:

AllProtosOn dummy mask with all 1's
BaselineCutoffs kludge needed to get cutoffs
PreTrainedTemplates kludge needed to get cutoffs

Note: Exceptions: none; History: Thu Mar 14 12:49:39 1991, DSJ, Created.

Definition at line 717 of file adaptmatch.cpp.

                                                            {
   FEATURE_SET Features;
   int Fid, Pid;
   FEATURE Feature;
   int NumFeatures;
   TEMP_PROTO TempProto;
   PROTO Proto;
   INT_CLASS IClass;
   TEMP_CONFIG Config;
 
   classify_norm_method.set_value(baseline);
   Features = ExtractOutlineFeatures(Blob);
   NumFeatures = Features->NumFeatures;
   if (NumFeatures > UNLIKELY_NUM_FEAT || NumFeatures <= 0) {
     FreeFeatureSet(Features);
     return;
   }
 
   Config = NewTempConfig(NumFeatures - 1, FontinfoId);
   TempConfigFor(Class, 0) = Config;
 
   /* this is a kludge to construct cutoffs for adapted templates */
   if (Templates == AdaptedTemplates)
     BaselineCutoffs[ClassId] = CharNormCutoffs[ClassId];
 
   IClass = ClassForClassId (Templates->Templates, ClassId);
 
   for (Fid = 0; Fid < Features->NumFeatures; Fid++) {
     Pid = AddIntProto (IClass);
     assert (Pid != NO_PROTO);
 
     Feature = Features->Features[Fid];
     TempProto = NewTempProto ();
     Proto = &(TempProto->Proto);
 
     /* compute proto params - NOTE that Y_DIM_OFFSET must be used because
        ConvertProto assumes that the Y dimension varies from -0.5 to 0.5
        instead of the -0.25 to 0.75 used in baseline normalization */
     Proto->Angle = Feature->Params[OutlineFeatDir];
     Proto->X = Feature->Params[OutlineFeatX];
     Proto->Y = Feature->Params[OutlineFeatY] - Y_DIM_OFFSET;
     Proto->Length = Feature->Params[OutlineFeatLength];
     FillABC(Proto);
 
     TempProto->ProtoId = Pid;
     SET_BIT (Config->Protos, Pid);
 
     ConvertProto(Proto, Pid, IClass);
     AddProtoToProtoPruner(Proto, Pid, IClass,
                           classify_learning_debug_level >= 2);
 
     Class->TempProtos = push (Class->TempProtos, TempProto);
   }
   FreeFeatureSet(Features);
 
   AddIntConfig(IClass);
   ConvertConfig (AllProtosOn, 0, IClass);
 
   if (classify_learning_debug_level >= 1) {
     tprintf("Added new class '%s' with class id %d and %d protos.\n",
             unicharset.id_to_unichar(ClassId), ClassId, NumFeatures);
     if (classify_learning_debug_level > 1)
       DisplayAdaptedChar(Blob, IClass);
   }
 
   if (IsEmptyAdaptedClass(Class))
     (Templates->NumNonEmptyClasses)++;
 }                                /* InitAdaptedClass */

◆ InitAdaptiveClassifier()

void tesseract::Classify::InitAdaptiveClassifier ( bool load_pre_trained_templates )

This routine reads in the training information needed by the adaptive classifier and saves it into global variables. Parameters: load_pre_trained_templates Indicates whether the pre-trained templates (inttemp, normproto and pffmtable components) should be loaded. Should only be set to true if the necessary classifier components are present in the [lang].traineddata file. Globals: BuiltInTemplatesFile file to get built-in temps from BuiltInCutoffsFile file to get avg. feat per class from classify_use_pre_adapted_templates enables use of pre-adapted templates

Note: History: Mon Mar 11 12:49:34 1991, DSJ, Created.

Definition at line 527 of file adaptmatch.cpp.

                                                                      {
   if (!classify_enable_adaptive_matcher)
     return;
   if (AllProtosOn != NULL)
     EndAdaptiveClassifier();  // Don't leak with multiple inits.
 
   // If there is no language_data_path_prefix, the classifier will be
   // adaptive only.
   if (language_data_path_prefix.length() > 0 &&
       load_pre_trained_templates) {
     ASSERT_HOST(tessdata_manager.SeekToStart(TESSDATA_INTTEMP));
     PreTrainedTemplates =
       ReadIntTemplates(tessdata_manager.GetDataFilePtr());
     if (tessdata_manager.DebugLevel() > 0) tprintf("Loaded inttemp\n");
 
     if (tessdata_manager.SeekToStart(TESSDATA_SHAPE_TABLE)) {
       shape_table_ = new ShapeTable(unicharset);
       if (!shape_table_->DeSerialize(tessdata_manager.swap(),
                                      tessdata_manager.GetDataFilePtr())) {
         tprintf("Error loading shape table!\n");
         delete shape_table_;
         shape_table_ = NULL;
       } else if (tessdata_manager.DebugLevel() > 0) {
         tprintf("Successfully loaded shape table!\n");
       }
     }
 
     ASSERT_HOST(tessdata_manager.SeekToStart(TESSDATA_PFFMTABLE));
     ReadNewCutoffs(tessdata_manager.GetDataFilePtr(),
                    tessdata_manager.swap(),
                    tessdata_manager.GetEndOffset(TESSDATA_PFFMTABLE),
                    CharNormCutoffs);
     if (tessdata_manager.DebugLevel() > 0) tprintf("Loaded pffmtable\n");
 
     ASSERT_HOST(tessdata_manager.SeekToStart(TESSDATA_NORMPROTO));
     NormProtos =
       ReadNormProtos(tessdata_manager.GetDataFilePtr(),
                      tessdata_manager.GetEndOffset(TESSDATA_NORMPROTO));
     if (tessdata_manager.DebugLevel() > 0) tprintf("Loaded normproto\n");
     static_classifier_ = new TessClassifier(false, this);
   }
 
   im_.Init(&classify_debug_level);
   InitIntegerFX();
 
   AllProtosOn = NewBitVector(MAX_NUM_PROTOS);
   AllConfigsOn = NewBitVector(MAX_NUM_CONFIGS);
   AllConfigsOff = NewBitVector(MAX_NUM_CONFIGS);
   TempProtoMask = NewBitVector(MAX_NUM_PROTOS);
   set_all_bits(AllProtosOn, WordsInVectorOfSize(MAX_NUM_PROTOS));
   set_all_bits(AllConfigsOn, WordsInVectorOfSize(MAX_NUM_CONFIGS));
   zero_all_bits(AllConfigsOff, WordsInVectorOfSize(MAX_NUM_CONFIGS));
 
   for (int i = 0; i < MAX_NUM_CLASSES; i++) {
      BaselineCutoffs[i] = 0;
   }
 
   if (classify_use_pre_adapted_templates) {
     FILE *File;
     STRING Filename;
 
     Filename = imagefile;
     Filename += ADAPT_TEMPLATE_SUFFIX;
     File = fopen(Filename.string(), "rb");
     if (File == NULL) {
       AdaptedTemplates = NewAdaptedTemplates(true);
     } else {
       cprintf("\nReading pre-adapted templates from %s ...\n",
               Filename.string());
       fflush(stdout);
       AdaptedTemplates = ReadAdaptedTemplates(File);
       cprintf("\n");
       fclose(File);
       PrintAdaptedTemplates(stdout, AdaptedTemplates);
 
       for (int i = 0; i < AdaptedTemplates->Templates->NumClasses; i++) {
         BaselineCutoffs[i] = CharNormCutoffs[i];
       }
     }
   } else {
     if (AdaptedTemplates != NULL)
       free_adapted_templates(AdaptedTemplates);
     AdaptedTemplates = NewAdaptedTemplates(true);
   }
 }                                /* InitAdaptiveClassifier */

◆ LargeSpeckle()

bool tesseract::Classify::LargeSpeckle ( const TBLOB & blob )

Definition at line 235 of file classify.cpp.

                                              {
   double speckle_size = kBlnXHeight * speckle_large_max_size;
   TBOX bbox = blob.bounding_box();
   return bbox.width() < speckle_size && bbox.height() < speckle_size;
 }

◆ LearnBlob()

void tesseract::Classify::LearnBlob	(	const STRING &	fontname,
		TBLOB *	Blob,
		const DENORM &	cn_denorm,
		const INT_FX_RESULT_STRUCT &	fx_info,
		const char *	blob_text
	)

Definition at line 69 of file blobclass.cpp.

                                                 {
   CHAR_DESC CharDesc = NewCharDescription(feature_defs_);
   CharDesc->FeatureSets[0] = ExtractMicros(blob, cn_denorm);
   CharDesc->FeatureSets[1] = ExtractCharNormFeatures(fx_info);
   CharDesc->FeatureSets[2] = ExtractIntCNFeatures(*blob, fx_info);
   CharDesc->FeatureSets[3] = ExtractIntGeoFeatures(*blob, fx_info);
 
   if (ValidCharDescription(feature_defs_, CharDesc)) {
     // Label the features with a class name and font name.
     tr_file_data_ += "\n";
     tr_file_data_ += fontname;
     tr_file_data_ += " ";
     tr_file_data_ += blob_text;
     tr_file_data_ += "\n";
 
     // write micro-features to file and clean up
     WriteCharDescription(feature_defs_, CharDesc, &tr_file_data_);
   } else {
     tprintf("Blob learned was invalid!\n");
   }
   FreeCharDescription(CharDesc);
 }                                // LearnBlob

◆ LearnPieces()

void tesseract::Classify::LearnPieces	(	const char *	fontname,
		int	start,
		int	length,
		float	threshold,
		CharSegmentationType	segmentation,
		const char *	correct_text,
		WERD_RES *	word
	)

Definition at line 368 of file adaptmatch.cpp.

                                                                      {
   // TODO(daria) Remove/modify this if/when we want
   // to train and/or adapt to n-grams.
   if (segmentation != CST_WHOLE &&
       (segmentation != CST_FRAGMENT || disable_character_fragments))
     return;
 
   if (length > 1) {
     SEAM::JoinPieces(word->seam_array, word->chopped_word->blobs, start,
                      start + length - 1);
   }
   TBLOB* blob = word->chopped_word->blobs[start];
   // Rotate the blob if needed for classification.
   TBLOB* rotated_blob = blob->ClassifyNormalizeIfNeeded();
   if (rotated_blob == NULL)
     rotated_blob = blob;
 
   #ifndef GRAPHICS_DISABLED
   // Draw debug windows showing the blob that is being learned if needed.
   if (strcmp(classify_learn_debug_str.string(), correct_text) == 0) {
     RefreshDebugWindow(&learn_debug_win_, "LearnPieces", 600,
                        word->chopped_word->bounding_box());
     rotated_blob->plot(learn_debug_win_, ScrollView::GREEN, ScrollView::BROWN);
     learn_debug_win_->Update();
     window_wait(learn_debug_win_);
   }
   if (classify_debug_character_fragments && segmentation == CST_FRAGMENT) {
     ASSERT_HOST(learn_fragments_debug_win_ != NULL);  // set up in LearnWord
     blob->plot(learn_fragments_debug_win_,
                ScrollView::BLUE, ScrollView::BROWN);
     learn_fragments_debug_win_->Update();
   }
   #endif  // GRAPHICS_DISABLED
 
   if (fontname != NULL) {
     classify_norm_method.set_value(character);  // force char norm spc 30/11/93
     tess_bn_matching.set_value(false);    // turn it off
     tess_cn_matching.set_value(false);
     DENORM bl_denorm, cn_denorm;
     INT_FX_RESULT_STRUCT fx_info;
     SetupBLCNDenorms(*rotated_blob, classify_nonlinear_norm,
                      &bl_denorm, &cn_denorm, &fx_info);
     LearnBlob(fontname, rotated_blob, cn_denorm, fx_info, correct_text);
   } else if (unicharset.contains_unichar(correct_text)) {
     UNICHAR_ID class_id = unicharset.unichar_to_id(correct_text);
     int font_id = word->fontinfo != NULL
                 ? fontinfo_table_.get_id(*word->fontinfo)
                 : 0;
     if (classify_learning_debug_level >= 1)
       tprintf("Adapting to char = %s, thr= %g font_id= %d\n",
               unicharset.id_to_unichar(class_id), threshold, font_id);
     // If filename is not NULL we are doing recognition
     // (as opposed to training), so we must have already set word fonts.
     AdaptToChar(rotated_blob, class_id, font_id, threshold, AdaptedTemplates);
     if (BackupAdaptedTemplates != NULL) {
       // Adapt the backup templates too. They will be used if the primary gets
       // too full.
       AdaptToChar(rotated_blob, class_id, font_id, threshold,
                   BackupAdaptedTemplates);
     }
   } else if (classify_debug_level >= 1) {
     tprintf("Can't adapt to %s not in unicharset\n", correct_text);
   }
   if (rotated_blob != blob) {
     delete rotated_blob;
   }
 
   SEAM::BreakPieces(word->seam_array, word->chopped_word->blobs, start,
                     start + length - 1);
 }  // LearnPieces.

◆ LearnWord()

void tesseract::Classify::LearnWord	(	const char *	fontname,
		WERD_RES *	word
	)

Definition at line 244 of file adaptmatch.cpp.

                                                              {
   int word_len = word->correct_text.size();
   if (word_len == 0) return;
 
   float* thresholds = NULL;
   if (fontname == NULL) {
     // Adaption mode.
     if (!EnableLearning || word->best_choice == NULL)
       return;  // Can't or won't adapt.
 
     if (classify_learning_debug_level >= 1)
       tprintf("\n\nAdapting to word = %s\n",
               word->best_choice->debug_string().string());
     thresholds = new float[word_len];
     word->ComputeAdaptionThresholds(certainty_scale,
                                     matcher_perfect_threshold,
                                     matcher_good_threshold,
                                     matcher_rating_margin, thresholds);
   }
   int start_blob = 0;
 
   #ifndef GRAPHICS_DISABLED
   if (classify_debug_character_fragments) {
     if (learn_fragmented_word_debug_win_ != NULL) {
       window_wait(learn_fragmented_word_debug_win_);
     }
     RefreshDebugWindow(&learn_fragments_debug_win_, "LearnPieces", 400,
                        word->chopped_word->bounding_box());
     RefreshDebugWindow(&learn_fragmented_word_debug_win_, "LearnWord", 200,
                        word->chopped_word->bounding_box());
     word->chopped_word->plot(learn_fragmented_word_debug_win_);
     ScrollView::Update();
   }
   #endif  // GRAPHICS_DISABLED
 
   for (int ch = 0; ch < word_len; ++ch) {
     if (classify_debug_character_fragments) {
       tprintf("\nLearning %s\n",  word->correct_text[ch].string());
     }
     if (word->correct_text[ch].length() > 0) {
       float threshold = thresholds != NULL ? thresholds[ch] : 0.0f;
 
       LearnPieces(fontname, start_blob, word->best_state[ch], threshold,
                   CST_WHOLE, word->correct_text[ch].string(), word);
 
       if (word->best_state[ch] > 1 && !disable_character_fragments) {
         // Check that the character breaks into meaningful fragments
         // that each match a whole character with at least
         // classify_character_fragments_garbage_certainty_threshold
         bool garbage = false;
         int frag;
         for (frag = 0; frag < word->best_state[ch]; ++frag) {
           TBLOB* frag_blob = word->chopped_word->blobs[start_blob + frag];
           if (classify_character_fragments_garbage_certainty_threshold < 0) {
             garbage |= LooksLikeGarbage(frag_blob);
           }
         }
         // Learn the fragments.
         if (!garbage) {
           bool pieces_all_natural = word->PiecesAllNatural(start_blob,
               word->best_state[ch]);
           if (pieces_all_natural || !prioritize_division) {
             for (frag = 0; frag < word->best_state[ch]; ++frag) {
               GenericVector<STRING> tokens;
               word->correct_text[ch].split(' ', &tokens);
 
               tokens[0] = CHAR_FRAGMENT::to_string(
                   tokens[0].string(), frag, word->best_state[ch],
                   pieces_all_natural);
 
               STRING full_string;
               for (int i = 0; i < tokens.size(); i++) {
                 full_string += tokens[i];
                 if (i != tokens.size() - 1)
                   full_string += ' ';
               }
               LearnPieces(fontname, start_blob + frag, 1, threshold,
                           CST_FRAGMENT, full_string.string(), word);
             }
           }
         }
       }
 
       // TODO(rays): re-enable this part of the code when we switch to the
       // new classifier that needs to see examples of garbage.
       /*
       if (word->best_state[ch] > 1) {
         // If the next blob is good, make junk with the rightmost fragment.
         if (ch + 1 < word_len && word->correct_text[ch + 1].length() > 0) {
           LearnPieces(fontname, start_blob + word->best_state[ch] - 1,
                       word->best_state[ch + 1] + 1,
                       threshold, CST_IMPROPER, INVALID_UNICHAR, word);
         }
         // If the previous blob is good, make junk with the leftmost fragment.
         if (ch > 0 && word->correct_text[ch - 1].length() > 0) {
           LearnPieces(fontname, start_blob - word->best_state[ch - 1],
                       word->best_state[ch - 1] + 1,
                       threshold, CST_IMPROPER, INVALID_UNICHAR, word);
         }
       }
       // If the next blob is good, make a join with it.
       if (ch + 1 < word_len && word->correct_text[ch + 1].length() > 0) {
         STRING joined_text = word->correct_text[ch];
         joined_text += word->correct_text[ch + 1];
         LearnPieces(fontname, start_blob,
                     word->best_state[ch] + word->best_state[ch + 1],
                     threshold, CST_NGRAM, joined_text.string(), word);
       }
       */
     }
     start_blob += word->best_state[ch];
   }
   delete [] thresholds;
 }  // LearnWord.

◆ LooksLikeGarbage()

bool tesseract::Classify::LooksLikeGarbage ( TBLOB * blob )

Definition at line 1681 of file adaptmatch.cpp.

                                            {
   BLOB_CHOICE_LIST *ratings = new BLOB_CHOICE_LIST();
   AdaptiveClassifier(blob, ratings);
   BLOB_CHOICE_IT ratings_it(ratings);
   const UNICHARSET &unicharset = getDict().getUnicharset();
   if (classify_debug_character_fragments) {
     print_ratings_list("======================\nLooksLikeGarbage() got ",
                        ratings, unicharset);
   }
   for (ratings_it.mark_cycle_pt(); !ratings_it.cycled_list();
        ratings_it.forward()) {
     if (unicharset.get_fragment(ratings_it.data()->unichar_id()) != NULL) {
       continue;
     }
     float certainty = ratings_it.data()->certainty();
     delete ratings;
     return certainty <
             classify_character_fragments_garbage_certainty_threshold;
   }
   delete ratings;
   return true;  // no whole characters in ratings
 }

◆ MakeNewTemporaryConfig()

int tesseract::Classify::MakeNewTemporaryConfig	(	ADAPT_TEMPLATES	Templates,
		CLASS_ID	ClassId,
		int	FontinfoId,
		int	NumFeatures,
		INT_FEATURE_ARRAY	Features,
		FEATURE_SET	FloatFeatures
	)

Parameters

Templates	adapted templates to add new config to
ClassId	class id to associate with new config
FontinfoId	font information inferred from pre-trained templates
NumFeatures	number of features in IntFeatures
Features	features describing model for new config
FloatFeatures	floating-pt representation of features

Returns: The id of the new config created, a negative integer in case of error.

Note: Exceptions: none; History: Fri Mar 15 08:49:46 1991, DSJ, Created.

Definition at line 1792 of file adaptmatch.cpp.

                                                       {
   INT_CLASS IClass;
   ADAPT_CLASS Class;
   PROTO_ID OldProtos[MAX_NUM_PROTOS];
   FEATURE_ID BadFeatures[MAX_NUM_INT_FEATURES];
   int NumOldProtos;
   int NumBadFeatures;
   int MaxProtoId, OldMaxProtoId;
   int BlobLength = 0;
   int MaskSize;
   int ConfigId;
   TEMP_CONFIG Config;
   int i;
   int debug_level = NO_DEBUG;
 
   if (classify_learning_debug_level >= 3)
     debug_level =
         PRINT_MATCH_SUMMARY | PRINT_FEATURE_MATCHES | PRINT_PROTO_MATCHES;
 
   IClass = ClassForClassId(Templates->Templates, ClassId);
   Class = Templates->Class[ClassId];
 
   if (IClass->NumConfigs >= MAX_NUM_CONFIGS) {
     ++NumAdaptationsFailed;
     if (classify_learning_debug_level >= 1)
       cprintf("Cannot make new temporary config: maximum number exceeded.\n");
     return -1;
   }
 
   OldMaxProtoId = IClass->NumProtos - 1;
 
   NumOldProtos = im_.FindGoodProtos(IClass, AllProtosOn, AllConfigsOff,
                                     BlobLength, NumFeatures, Features,
                                     OldProtos, classify_adapt_proto_threshold,
                                     debug_level);
 
   MaskSize = WordsInVectorOfSize(MAX_NUM_PROTOS);
   zero_all_bits(TempProtoMask, MaskSize);
   for (i = 0; i < NumOldProtos; i++)
     SET_BIT(TempProtoMask, OldProtos[i]);
 
   NumBadFeatures = im_.FindBadFeatures(IClass, TempProtoMask, AllConfigsOn,
                                        BlobLength, NumFeatures, Features,
                                        BadFeatures,
                                        classify_adapt_feature_threshold,
                                        debug_level);
 
   MaxProtoId = MakeNewTempProtos(FloatFeatures, NumBadFeatures, BadFeatures,
                                  IClass, Class, TempProtoMask);
   if (MaxProtoId == NO_PROTO) {
     ++NumAdaptationsFailed;
     if (classify_learning_debug_level >= 1)
       cprintf("Cannot make new temp protos: maximum number exceeded.\n");
     return -1;
   }
 
   ConfigId = AddIntConfig(IClass);
   ConvertConfig(TempProtoMask, ConfigId, IClass);
   Config = NewTempConfig(MaxProtoId, FontinfoId);
   TempConfigFor(Class, ConfigId) = Config;
   copy_all_bits(TempProtoMask, Config->Protos, Config->ProtoVectorSize);
 
   if (classify_learning_debug_level >= 1)
     cprintf("Making new temp config %d fontinfo id %d"
             " using %d old and %d new protos.\n",
             ConfigId, Config->FontinfoId,
             NumOldProtos, MaxProtoId - OldMaxProtoId);
 
   return ConfigId;
 }                              /* MakeNewTemporaryConfig */

◆ MakeNewTempProtos()

PROTO_ID tesseract::Classify::MakeNewTempProtos	(	FEATURE_SET	Features,
		int	NumBadFeat,
		FEATURE_ID	BadFeat[],
		INT_CLASS	IClass,
		ADAPT_CLASS	Class,
		BIT_VECTOR	TempProtoMask
	)

This routine finds sets of sequential bad features that all have the same angle and converts each set into a new temporary proto. The temp proto is added to the proto pruner for IClass, pushed onto the list of temp protos in Class, and added to TempProtoMask.

Parameters

Features	floating-pt features describing new character
NumBadFeat	number of bad features to turn into protos
BadFeat	feature id's of bad features
IClass	integer class templates to add new protos to
Class	adapted class templates to add new protos to
TempProtoMask	proto mask to add new protos to

Globals: none

Returns: Max proto id in class after all protos have been added. Exceptions: none History: Fri Mar 15 11:39:38 1991, DSJ, Created.

Definition at line 1889 of file adaptmatch.cpp.

                                                                {
   FEATURE_ID *ProtoStart;
   FEATURE_ID *ProtoEnd;
   FEATURE_ID *LastBad;
   TEMP_PROTO TempProto;
   PROTO Proto;
   FEATURE F1, F2;
   FLOAT32 X1, X2, Y1, Y2;
   FLOAT32 A1, A2, AngleDelta;
   FLOAT32 SegmentLength;
   PROTO_ID Pid;
 
   for (ProtoStart = BadFeat, LastBad = ProtoStart + NumBadFeat;
        ProtoStart < LastBad; ProtoStart = ProtoEnd) {
     F1 = Features->Features[*ProtoStart];
     X1 = F1->Params[PicoFeatX];
     Y1 = F1->Params[PicoFeatY];
     A1 = F1->Params[PicoFeatDir];
 
     for (ProtoEnd = ProtoStart + 1,
          SegmentLength = GetPicoFeatureLength();
          ProtoEnd < LastBad;
          ProtoEnd++, SegmentLength += GetPicoFeatureLength()) {
       F2 = Features->Features[*ProtoEnd];
       X2 = F2->Params[PicoFeatX];
       Y2 = F2->Params[PicoFeatY];
       A2 = F2->Params[PicoFeatDir];
 
       AngleDelta = fabs(A1 - A2);
       if (AngleDelta > 0.5)
         AngleDelta = 1.0 - AngleDelta;
 
       if (AngleDelta > matcher_clustering_max_angle_delta ||
           fabs(X1 - X2) > SegmentLength ||
           fabs(Y1 - Y2) > SegmentLength)
         break;
     }
 
     F2 = Features->Features[*(ProtoEnd - 1)];
     X2 = F2->Params[PicoFeatX];
     Y2 = F2->Params[PicoFeatY];
     A2 = F2->Params[PicoFeatDir];
 
     Pid = AddIntProto(IClass);
     if (Pid == NO_PROTO)
       return (NO_PROTO);
 
     TempProto = NewTempProto();
     Proto = &(TempProto->Proto);
 
     /* compute proto params - NOTE that Y_DIM_OFFSET must be used because
        ConvertProto assumes that the Y dimension varies from -0.5 to 0.5
        instead of the -0.25 to 0.75 used in baseline normalization */
     Proto->Length = SegmentLength;
     Proto->Angle = A1;
     Proto->X = (X1 + X2) / 2.0;
     Proto->Y = (Y1 + Y2) / 2.0 - Y_DIM_OFFSET;
     FillABC(Proto);
 
     TempProto->ProtoId = Pid;
     SET_BIT(TempProtoMask, Pid);
 
     ConvertProto(Proto, Pid, IClass);
     AddProtoToProtoPruner(Proto, Pid, IClass,
                           classify_learning_debug_level >= 2);
 
     Class->TempProtos = push(Class->TempProtos, TempProto);
   }
   return IClass->NumProtos - 1;
 }                              /* MakeNewTempProtos */

◆ MakePermanent()

void tesseract::Classify::MakePermanent	(	ADAPT_TEMPLATES	Templates,
		CLASS_ID	ClassId,
		int	ConfigId,
		TBLOB *	Blob
	)

Parameters

Templates	current set of adaptive templates
ClassId	class containing config to be made permanent
ConfigId	config to be made permanent
Blob	current blob being adapted to

Globals: none

Note: Exceptions: none; History: Thu Mar 14 15:54:08 1991, DSJ, Created.

Definition at line 1978 of file adaptmatch.cpp.

                                           {
   UNICHAR_ID *Ambigs;
   TEMP_CONFIG Config;
   ADAPT_CLASS Class;
   PROTO_KEY ProtoKey;
 
   Class = Templates->Class[ClassId];
   Config = TempConfigFor(Class, ConfigId);
 
   MakeConfigPermanent(Class, ConfigId);
   if (Class->NumPermConfigs == 0)
     Templates->NumPermClasses++;
   Class->NumPermConfigs++;
 
   // Initialize permanent config.
   Ambigs = GetAmbiguities(Blob, ClassId);
   PERM_CONFIG Perm = (PERM_CONFIG) alloc_struct(sizeof(PERM_CONFIG_STRUCT),
                                                 "PERM_CONFIG_STRUCT");
   Perm->Ambigs = Ambigs;
   Perm->FontinfoId = Config->FontinfoId;
 
   // Free memory associated with temporary config (since ADAPTED_CONFIG
   // is a union we need to clean up before we record permanent config).
   ProtoKey.Templates = Templates;
   ProtoKey.ClassId = ClassId;
   ProtoKey.ConfigId = ConfigId;
   Class->TempProtos = delete_d(Class->TempProtos, &ProtoKey, MakeTempProtoPerm);
   FreeTempConfig(Config);
 
   // Record permanent config.
   PermConfigFor(Class, ConfigId) = Perm;
 
   if (classify_learning_debug_level >= 1) {
     tprintf("Making config %d for %s (ClassId %d) permanent:"
             " fontinfo id %d, ambiguities '",
             ConfigId, getDict().getUnicharset().debug_str(ClassId).string(),
             ClassId, PermConfigFor(Class, ConfigId)->FontinfoId);
     for (UNICHAR_ID *AmbigsPointer = Ambigs;
         *AmbigsPointer >= 0; ++AmbigsPointer)
       tprintf("%s", unicharset.id_to_unichar(*AmbigsPointer));
     tprintf("'.\n");
   }
 }                              /* MakePermanent */

◆ MasterMatcher()

void tesseract::Classify::MasterMatcher	(	INT_TEMPLATES	templates,
		inT16	num_features,
		const INT_FEATURE_STRUCT *	features,
		const uinT8 *	norm_factors,
		ADAPT_CLASS *	classes,
		int	debug,
		int	matcher_multiplier,
		const TBOX &	blob_box,
		const GenericVector< CP_RESULT_STRUCT > &	results,
		ADAPT_RESULTS *	final_results
	)

Factored-out calls to IntegerMatcher based on class pruner results. Returns integer matcher results inside CLASS_PRUNER_RESULTS structure.

Definition at line 1127 of file adaptmatch.cpp.

                                                            {
   int top = blob_box.top();
   int bottom = blob_box.bottom();
   UnicharRating int_result;
   for (int c = 0; c < results.size(); c++) {
     CLASS_ID class_id = results[c].Class;
     BIT_VECTOR protos = classes != NULL ? classes[class_id]->PermProtos
                                         : AllProtosOn;
     BIT_VECTOR configs = classes != NULL ? classes[class_id]->PermConfigs
                                          : AllConfigsOn;
 
     int_result.unichar_id = class_id;
     im_.Match(ClassForClassId(templates, class_id),
               protos, configs,
               num_features, features,
               &int_result, classify_adapt_feature_threshold, debug,
               matcher_debug_separate_windows);
     bool debug = matcher_debug_level >= 2 || classify_debug_level > 1;
     ExpandShapesAndApplyCorrections(classes, debug, class_id, bottom, top,
                                     results[c].Rating,
                                     final_results->BlobLength,
                                     matcher_multiplier, norm_factors,
                                     &int_result, final_results);
   }
 }

◆ NewAdaptedTemplates()

ADAPT_TEMPLATES tesseract::Classify::NewAdaptedTemplates ( bool InitFromUnicharset )

Allocates memory for adapted tempates. each char in unicharset to the newly created templates

Parameters

InitFromUnicharset if true, add an empty class for

Returns: Ptr to new adapted templates.

Note: Globals: none; Exceptions: none; History: Fri Mar 8 10:15:28 1991, DSJ, Created.

Definition at line 167 of file adaptive.cpp.

                                                                      {
   ADAPT_TEMPLATES Templates;
   int i;
 
   Templates = (ADAPT_TEMPLATES) Emalloc (sizeof (ADAPT_TEMPLATES_STRUCT));
 
   Templates->Templates = NewIntTemplates ();
   Templates->NumPermClasses = 0;
   Templates->NumNonEmptyClasses = 0;
 
   /* Insert an empty class for each unichar id in unicharset */
   for (i = 0; i < MAX_NUM_CLASSES; i++) {
     Templates->Class[i] = NULL;
     if (InitFromUnicharset && i < unicharset.size()) {
       AddAdaptedClass(Templates, NewAdaptedClass(), i);
     }
   }
 
   return (Templates);
 
 }                                /* NewAdaptedTemplates */

◆ NormalizeOutlines()

void tesseract::Classify::NormalizeOutlines	(	LIST	Outlines,
		FLOAT32 *	XScale,
		FLOAT32 *	YScale
	)

This routine normalizes every outline in Outlines according to the currently selected normalization method. It also returns the scale factors that it used to do this scaling. The scale factors returned represent the x and y sizes in the normalized coordinate system that correspond to 1 pixel in the original coordinate system.

Globals:

classify_norm_method method being used for normalization
classify_char_norm_range map radius of gyration to this value
Parameters

Outlines list of outlines to be normalized

XScale x-direction scale factor used by routine

YScale y-direction scale factor used by routine

Returns
none (Outlines are changed and XScale and YScale are updated)

Note
Exceptions: none

History: Fri Dec 14 08:14:55 1990, DSJ, Created.

Definition at line 301 of file mfoutline.cpp.

                                                   {
   MFOUTLINE Outline;
 
   switch (classify_norm_method) {
     case character:
       ASSERT_HOST(!"How did NormalizeOutlines get called in character mode?");
       break;
 
     case baseline:
       iterate(Outlines) {
         Outline = (MFOUTLINE) first_node(Outlines);
         NormalizeOutline(Outline, 0.0);
       }
       *XScale = *YScale = MF_SCALE_FACTOR;
       break;
   }
 }                                /* NormalizeOutlines */

◆ PrintAdaptedTemplates()

void tesseract::Classify::PrintAdaptedTemplates	(	FILE *	File,
		ADAPT_TEMPLATES	Templates
	)

This routine prints a summary of the adapted templates in Templates to File.

Parameters

File	open text file to print Templates to
Templates	adapted templates to print to File

Note: Globals: none; Exceptions: none; History: Wed Mar 20 13:35:29 1991, DSJ, Created.

Definition at line 273 of file adaptive.cpp.

                                                                           {
   int i;
   INT_CLASS IClass;
   ADAPT_CLASS AClass;
 
   fprintf (File, "\n\nSUMMARY OF ADAPTED TEMPLATES:\n\n");
   fprintf (File, "Num classes = %d;  Num permanent classes = %d\n\n",
            Templates->NumNonEmptyClasses, Templates->NumPermClasses);
   fprintf (File, "   Id  NC NPC  NP NPP\n");
   fprintf (File, "------------------------\n");
 
   for (i = 0; i < (Templates->Templates)->NumClasses; i++) {
     IClass = Templates->Templates->Class[i];
     AClass = Templates->Class[i];
     if (!IsEmptyAdaptedClass (AClass)) {
       fprintf (File, "%5d  %s %3d %3d %3d %3d\n",
         i, unicharset.id_to_unichar(i),
       IClass->NumConfigs, AClass->NumPermConfigs,
       IClass->NumProtos,
       IClass->NumProtos - count (AClass->TempProtos));
     }
   }
   fprintf (File, "\n");
 
 }                                /* PrintAdaptedTemplates */

◆ PrintAdaptiveMatchResults()

void tesseract::Classify::PrintAdaptiveMatchResults ( const ADAPT_RESULTS & results )

This routine writes the matches in Results to File.

Parameters

results match results to write to File

Globals: none

Note: Exceptions: none; History: Mon Mar 18 09:24:53 1991, DSJ, Created.

Definition at line 2077 of file adaptmatch.cpp.

                                                                      {
   for (int i = 0; i < results.match.size(); ++i) {
     tprintf("%s  ", unicharset.debug_str(results.match[i].unichar_id).string());
     results.match[i].Print();
   }
 }                              /* PrintAdaptiveMatchResults */

◆ PruneClasses()

int tesseract::Classify::PruneClasses	(	const INT_TEMPLATES_STRUCT *	int_templates,
		int	num_features,
		int	keep_this,
		const INT_FEATURE_STRUCT *	features,
		const uinT8 *	normalization_factors,
		const uinT16 *	expected_num_features,
		GenericVector< CP_RESULT_STRUCT > *	results
	)

Runs the class pruner from int_templates on the given features, returning the number of classes output in results.

Parameters

int_templates	Class pruner tables
num_features	Number of features in blob
features	Array of features
normalization_factors	Array of fudge factors from blob normalization process (by CLASS_INDEX)
expected_num_features	Array of expected number of features for each class (by CLASS_INDEX)
results	Sorted Array of pruned classes. Must be an array of size at least int_templates->NumClasses.
keep_this

Definition at line 412 of file intmatcher.cpp.

                                                                      {
   ClassPruner pruner(int_templates->NumClasses);
   // Compute initial match scores for all classes.
   pruner.ComputeScores(int_templates, num_features, features);
   // Adjust match scores for number of expected features.
   pruner.AdjustForExpectedNumFeatures(expected_num_features,
                                       classify_cp_cutoff_strength);
   // Apply disabled classes in unicharset - only works without a shape_table.
   if (shape_table_ == NULL)
     pruner.DisableDisabledClasses(unicharset);
   // If fragments are disabled, remove them, also only without a shape table.
   if (disable_character_fragments && shape_table_ == NULL)
     pruner.DisableFragments(unicharset);
 
   // If we have good x-heights, apply the given normalization factors.
   if (normalization_factors != NULL) {
     pruner.NormalizeForXheight(classify_class_pruner_multiplier,
                                normalization_factors);
   } else {
     pruner.NoNormalization();
   }
   // Do the actual pruning and sort the short-list.
   pruner.PruneAndSort(classify_class_pruner_threshold, keep_this,
                       shape_table_ == NULL, unicharset);
 
   if (classify_debug_level > 2) {
     pruner.DebugMatch(*this, int_templates, features);
   }
   if (classify_debug_level > 1) {
     pruner.SummarizeResult(*this, int_templates, expected_num_features,
                            classify_class_pruner_multiplier,
                            normalization_factors);
   }
   // Convert to the expected output format.
   return pruner.SetupResults(results);
 }

◆ ReadAdaptedTemplates()

ADAPT_TEMPLATES tesseract::Classify::ReadAdaptedTemplates ( FILE * File )

Read a set of adapted templates from File and return a ptr to the templates.

Parameters

File	open text file to read adapted templates from

Returns: Ptr to adapted templates read from File.

Note: Globals: none; Exceptions: none; History: Mon Mar 18 15:18:10 1991, DSJ, Created.

Definition at line 369 of file adaptive.cpp.

                                                          {
   int i;
   ADAPT_TEMPLATES Templates;
 
   /* first read the high level adaptive template struct */
   Templates = (ADAPT_TEMPLATES) Emalloc (sizeof (ADAPT_TEMPLATES_STRUCT));
   fread ((char *) Templates, sizeof (ADAPT_TEMPLATES_STRUCT), 1, File);
 
   /* then read in the basic integer templates */
   Templates->Templates = ReadIntTemplates (File);
 
   /* then read in the adaptive info for each class */
   for (i = 0; i < (Templates->Templates)->NumClasses; i++) {
     Templates->Class[i] = ReadAdaptedClass (File);
   }
   return (Templates);
 
 }                                /* ReadAdaptedTemplates */

◆ ReadIntTemplates()

INT_TEMPLATES tesseract::Classify::ReadIntTemplates ( FILE * File )

This routine reads a set of integer templates from File. File must already be open and must be in the correct binary format.

Parameters

File	open file to read templates from

Returns: Pointer to integer templates read from File.

Note: Globals: none; Exceptions: none; History: Wed Feb 27 11:48:46 1991, DSJ, Created.

Definition at line 761 of file intproto.cpp.

                                                    {
   int i, j, w, x, y, z;
   BOOL8 swap;
   int nread;
   int unicharset_size;
   int version_id = 0;
   INT_TEMPLATES Templates;
   CLASS_PRUNER_STRUCT* Pruner;
   INT_CLASS Class;
   uinT8 *Lengths;
   PROTO_SET ProtoSet;
 
   /* variables for conversion from older inttemp formats */
   int b, bit_number, last_cp_bit_number, new_b, new_i, new_w;
   CLASS_ID class_id, max_class_id;
   inT16 *IndexFor = new inT16[MAX_NUM_CLASSES];
   CLASS_ID *ClassIdFor = new CLASS_ID[MAX_NUM_CLASSES];
   CLASS_PRUNER_STRUCT **TempClassPruner =
       new CLASS_PRUNER_STRUCT*[MAX_NUM_CLASS_PRUNERS];
   uinT32 SetBitsForMask =           // word with NUM_BITS_PER_CLASS
     (1 << NUM_BITS_PER_CLASS) - 1;  // set starting at bit 0
   uinT32 Mask, NewMask, ClassBits;
   int MaxNumConfigs = MAX_NUM_CONFIGS;
   int WerdsPerConfigVec = WERDS_PER_CONFIG_VEC;
 
   /* first read the high level template struct */
   Templates = NewIntTemplates();
   // Read Templates in parts for 64 bit compatibility.
   if (fread(&unicharset_size, sizeof(int), 1, File) != 1)
     cprintf("Bad read of inttemp!\n");
   if (fread(&Templates->NumClasses,
             sizeof(Templates->NumClasses), 1, File) != 1 ||
       fread(&Templates->NumClassPruners,
             sizeof(Templates->NumClassPruners), 1, File) != 1)
     cprintf("Bad read of inttemp!\n");
   // Swap status is determined automatically.
   swap = Templates->NumClassPruners < 0 ||
     Templates->NumClassPruners > MAX_NUM_CLASS_PRUNERS;
   if (swap) {
     Reverse32(&Templates->NumClassPruners);
     Reverse32(&Templates->NumClasses);
     Reverse32(&unicharset_size);
   }
   if (Templates->NumClasses < 0) {
     // This file has a version id!
     version_id = -Templates->NumClasses;
     if (fread(&Templates->NumClasses, sizeof(Templates->NumClasses),
               1, File) != 1)
       cprintf("Bad read of inttemp!\n");
     if (swap)
       Reverse32(&Templates->NumClasses);
   }
 
   if (version_id < 3) {
     MaxNumConfigs = OLD_MAX_NUM_CONFIGS;
     WerdsPerConfigVec = OLD_WERDS_PER_CONFIG_VEC;
   }
 
   if (version_id < 2) {
     for (i = 0; i < unicharset_size; ++i) {
       if (fread(&IndexFor[i], sizeof(inT16), 1, File) != 1)
         cprintf("Bad read of inttemp!\n");
     }
     for (i = 0; i < Templates->NumClasses; ++i) {
       if (fread(&ClassIdFor[i], sizeof(CLASS_ID), 1, File) != 1)
         cprintf("Bad read of inttemp!\n");
     }
     if (swap) {
       for (i = 0; i < Templates->NumClasses; i++)
         Reverse16(&IndexFor[i]);
       for (i = 0; i < Templates->NumClasses; i++)
         Reverse32(&ClassIdFor[i]);
     }
   }
 
   /* then read in the class pruners */
   for (i = 0; i < Templates->NumClassPruners; i++) {
     Pruner = new CLASS_PRUNER_STRUCT;
     if ((nread =
          fread(Pruner, 1, sizeof(CLASS_PRUNER_STRUCT),
                 File)) != sizeof(CLASS_PRUNER_STRUCT))
       cprintf("Bad read of inttemp!\n");
     if (swap) {
       for (x = 0; x < NUM_CP_BUCKETS; x++) {
         for (y = 0; y < NUM_CP_BUCKETS; y++) {
           for (z = 0; z < NUM_CP_BUCKETS; z++) {
             for (w = 0; w < WERDS_PER_CP_VECTOR; w++) {
               Reverse32(&Pruner->p[x][y][z][w]);
             }
           }
         }
       }
     }
     if (version_id < 2) {
       TempClassPruner[i] = Pruner;
     } else {
       Templates->ClassPruners[i] = Pruner;
     }
   }
 
   /* fix class pruners if they came from an old version of inttemp */
   if (version_id < 2) {
     // Allocate enough class pruners to cover all the class ids.
     max_class_id = 0;
     for (i = 0; i < Templates->NumClasses; i++)
       if (ClassIdFor[i] > max_class_id)
         max_class_id = ClassIdFor[i];
     for (i = 0; i <= CPrunerIdFor(max_class_id); i++) {
       Templates->ClassPruners[i] = new CLASS_PRUNER_STRUCT;
       memset(Templates->ClassPruners[i], 0, sizeof(CLASS_PRUNER_STRUCT));
     }
     // Convert class pruners from the old format (indexed by class index)
     // to the new format (indexed by class id).
     last_cp_bit_number = NUM_BITS_PER_CLASS * Templates->NumClasses - 1;
     for (i = 0; i < Templates->NumClassPruners; i++) {
       for (x = 0; x < NUM_CP_BUCKETS; x++)
         for (y = 0; y < NUM_CP_BUCKETS; y++)
           for (z = 0; z < NUM_CP_BUCKETS; z++)
             for (w = 0; w < WERDS_PER_CP_VECTOR; w++) {
               if (TempClassPruner[i]->p[x][y][z][w] == 0)
                 continue;
               for (b = 0; b < BITS_PER_WERD; b += NUM_BITS_PER_CLASS) {
                 bit_number = i * BITS_PER_CP_VECTOR + w * BITS_PER_WERD + b;
                 if (bit_number > last_cp_bit_number)
                   break; // the rest of the bits in this word are not used
                 class_id = ClassIdFor[bit_number / NUM_BITS_PER_CLASS];
                 // Single out NUM_BITS_PER_CLASS bits relating to class_id.
                 Mask = SetBitsForMask << b;
                 ClassBits = TempClassPruner[i]->p[x][y][z][w] & Mask;
                 // Move these bits to the new position in which they should
                 // appear (indexed corresponding to the class_id).
                 new_i = CPrunerIdFor(class_id);
                 new_w = CPrunerWordIndexFor(class_id);
                 new_b = CPrunerBitIndexFor(class_id) * NUM_BITS_PER_CLASS;
                 if (new_b > b) {
                   ClassBits <<= (new_b - b);
                 } else {
                   ClassBits >>= (b - new_b);
                 }
                 // Copy bits relating to class_id to the correct position
                 // in Templates->ClassPruner.
                 NewMask = SetBitsForMask << new_b;
                 Templates->ClassPruners[new_i]->p[x][y][z][new_w] &= ~NewMask;
                 Templates->ClassPruners[new_i]->p[x][y][z][new_w] |= ClassBits;
               }
             }
     }
     for (i = 0; i < Templates->NumClassPruners; i++) {
       delete TempClassPruner[i];
     }
   }
 
   /* then read in each class */
   for (i = 0; i < Templates->NumClasses; i++) {
     /* first read in the high level struct for the class */
     Class = (INT_CLASS) Emalloc (sizeof (INT_CLASS_STRUCT));
     if (fread(&Class->NumProtos, sizeof(Class->NumProtos), 1, File) != 1 ||
         fread(&Class->NumProtoSets, sizeof(Class->NumProtoSets), 1, File) != 1 ||
         fread(&Class->NumConfigs, sizeof(Class->NumConfigs), 1, File) != 1)
       cprintf ("Bad read of inttemp!\n");
     if (version_id == 0) {
       // Only version 0 writes 5 pointless pointers to the file.
       for (j = 0; j < 5; ++j) {
         int junk;
         if (fread(&junk, sizeof(junk), 1, File) != 1)
           cprintf ("Bad read of inttemp!\n");
       }
     }
     if (version_id < 4) {
       for (j = 0; j < MaxNumConfigs; ++j) {
         if (fread(&Class->ConfigLengths[j], sizeof(uinT16), 1, File) != 1)
           cprintf ("Bad read of inttemp!\n");
       }
       if (swap) {
         Reverse16(&Class->NumProtos);
         for (j = 0; j < MaxNumConfigs; j++)
           Reverse16(&Class->ConfigLengths[j]);
       }
     } else {
       ASSERT_HOST(Class->NumConfigs < MaxNumConfigs);
       for (j = 0; j < Class->NumConfigs; ++j) {
         if (fread(&Class->ConfigLengths[j], sizeof(uinT16), 1, File) != 1)
           cprintf ("Bad read of inttemp!\n");
       }
       if (swap) {
         Reverse16(&Class->NumProtos);
         for (j = 0; j < MaxNumConfigs; j++)
           Reverse16(&Class->ConfigLengths[j]);
       }
     }
     if (version_id < 2) {
       ClassForClassId (Templates, ClassIdFor[i]) = Class;
     } else {
       ClassForClassId (Templates, i) = Class;
     }
 
     /* then read in the proto lengths */
     Lengths = NULL;
     if (MaxNumIntProtosIn (Class) > 0) {
       Lengths = (uinT8 *)Emalloc(sizeof(uinT8) * MaxNumIntProtosIn(Class));
       if ((nread =
            fread((char *)Lengths, sizeof(uinT8),
                  MaxNumIntProtosIn(Class), File)) != MaxNumIntProtosIn (Class))
         cprintf ("Bad read of inttemp!\n");
     }
     Class->ProtoLengths = Lengths;
 
     /* then read in the proto sets */
     for (j = 0; j < Class->NumProtoSets; j++) {
       ProtoSet = (PROTO_SET)Emalloc(sizeof(PROTO_SET_STRUCT));
       if (version_id < 3) {
         if ((nread =
              fread((char *) &ProtoSet->ProtoPruner, 1,
                     sizeof(PROTO_PRUNER), File)) != sizeof(PROTO_PRUNER))
           cprintf("Bad read of inttemp!\n");
         for (x = 0; x < PROTOS_PER_PROTO_SET; x++) {
           if ((nread = fread((char *) &ProtoSet->Protos[x].A, 1,
                              sizeof(inT8), File)) != sizeof(inT8) ||
               (nread = fread((char *) &ProtoSet->Protos[x].B, 1,
                              sizeof(uinT8), File)) != sizeof(uinT8) ||
               (nread = fread((char *) &ProtoSet->Protos[x].C, 1,
                              sizeof(inT8), File)) != sizeof(inT8) ||
               (nread = fread((char *) &ProtoSet->Protos[x].Angle, 1,
                              sizeof(uinT8), File)) != sizeof(uinT8))
             cprintf("Bad read of inttemp!\n");
           for (y = 0; y < WerdsPerConfigVec; y++)
             if ((nread = fread((char *) &ProtoSet->Protos[x].Configs[y], 1,
                                sizeof(uinT32), File)) != sizeof(uinT32))
               cprintf("Bad read of inttemp!\n");
         }
       } else {
         if ((nread =
              fread((char *) ProtoSet, 1, sizeof(PROTO_SET_STRUCT),
                    File)) != sizeof(PROTO_SET_STRUCT))
           cprintf("Bad read of inttemp!\n");
       }
       if (swap) {
         for (x = 0; x < NUM_PP_PARAMS; x++)
           for (y = 0; y < NUM_PP_BUCKETS; y++)
             for (z = 0; z < WERDS_PER_PP_VECTOR; z++)
               Reverse32(&ProtoSet->ProtoPruner[x][y][z]);
         for (x = 0; x < PROTOS_PER_PROTO_SET; x++)
           for (y = 0; y < WerdsPerConfigVec; y++)
             Reverse32(&ProtoSet->Protos[x].Configs[y]);
       }
       Class->ProtoSets[j] = ProtoSet;
     }
     if (version_id < 4)
       Class->font_set_id = -1;
     else {
       fread(&Class->font_set_id, sizeof(int), 1, File);
       if (swap)
         Reverse32(&Class->font_set_id);
     }
   }
 
   if (version_id < 2) {
     /* add an empty NULL class with class id 0 */
     assert(UnusedClassIdIn (Templates, 0));
     ClassForClassId (Templates, 0) = NewIntClass (1, 1);
     ClassForClassId (Templates, 0)->font_set_id = -1;
     Templates->NumClasses++;
     /* make sure the classes are contiguous */
     for (i = 0; i < MAX_NUM_CLASSES; i++) {
       if (i < Templates->NumClasses) {
         if (ClassForClassId (Templates, i) == NULL) {
           fprintf(stderr, "Non-contiguous class ids in inttemp\n");
           exit(1);
         }
       } else {
         if (ClassForClassId (Templates, i) != NULL) {
           fprintf(stderr, "Class id %d exceeds NumClassesIn (Templates) %d\n",
                   i, Templates->NumClasses);
           exit(1);
         }
       }
     }
   }
   if (version_id >= 4) {
     this->fontinfo_table_.read(File, NewPermanentTessCallback(read_info), swap);
     if (version_id >= 5) {
       this->fontinfo_table_.read(File,
                                  NewPermanentTessCallback(read_spacing_info),
                                  swap);
     }
     this->fontset_table_.read(File, NewPermanentTessCallback(read_set), swap);
   }
 
   // Clean up.
   delete[] IndexFor;
   delete[] ClassIdFor;
   delete[] TempClassPruner;
 
   return (Templates);
 }                                /* ReadIntTemplates */

◆ ReadNewCutoffs()

void tesseract::Classify::ReadNewCutoffs	(	FILE *	CutoffFile,
		bool	swap,
		inT64	end_offset,
		CLASS_CUTOFF_ARRAY	Cutoffs
	)

Open Filename, read in all of the class-id/cutoff pairs and insert them into the Cutoffs array. Cutoffs are indexed in the array by class id. Unused entries in the array are set to an arbitrarily high cutoff value.

Parameters

CutoffFile	name of file containing cutoff definitions
Cutoffs	array to put cutoffs into
swap
end_offset

Returns: none

Note: Globals: none; Exceptions: none; History: Wed Feb 20 09:38:26 1991, DSJ, Created.

Definition at line 52 of file cutoffs.cpp.

                                                           {
   char Class[UNICHAR_LEN + 1];
   CLASS_ID ClassId;
   int Cutoff;
   int i;
 
   if (shape_table_ != NULL) {
     if (!shapetable_cutoffs_.DeSerialize(swap, CutoffFile)) {
       tprintf("Error during read of shapetable pffmtable!\n");
     }
   }
   for (i = 0; i < MAX_NUM_CLASSES; i++)
     Cutoffs[i] = MAX_CUTOFF;
 
   while ((end_offset < 0 || ftell(CutoffFile) < end_offset) &&
          tfscanf(CutoffFile, "%" REALLY_QUOTE_IT(UNICHAR_LEN) "s %d",
                 Class, &Cutoff) == 2) {
     if (strcmp(Class, "NULL") == 0) {
       ClassId = unicharset.unichar_to_id(" ");
     } else {
       ClassId = unicharset.unichar_to_id(Class);
     }
     Cutoffs[ClassId] = Cutoff;
     SkipNewline(CutoffFile);
   }
 }

◆ ReadNormProtos()

NORM_PROTOS * tesseract::Classify::ReadNormProtos	(	FILE *	File,
		inT64	end_offset
	)

This routine allocates a new data structure to hold a set of character normalization protos. It then fills in the data structure by reading from the specified File.

Parameters

File	open text file to read normalization protos from
end_offset	Globals: none

Returns: Character normalization protos.

Note: Exceptions: none; History: Wed Dec 19 16:38:49 1990, DSJ, Created.

Definition at line 245 of file normmatch.cpp.

                                                                   {
   NORM_PROTOS *NormProtos;
   int i;
   char unichar[2 * UNICHAR_LEN + 1];
   UNICHAR_ID unichar_id;
   LIST Protos;
   int NumProtos;
 
   /* allocate and initialization data structure */
   NormProtos = (NORM_PROTOS *) Emalloc (sizeof (NORM_PROTOS));
   NormProtos->NumProtos = unicharset.size();
   NormProtos->Protos = (LIST *) Emalloc (NormProtos->NumProtos * sizeof(LIST));
   for (i = 0; i < NormProtos->NumProtos; i++)
     NormProtos->Protos[i] = NIL_LIST;
 
   /* read file header and save in data structure */
   NormProtos->NumParams = ReadSampleSize (File);
   NormProtos->ParamDesc = ReadParamDesc (File, NormProtos->NumParams);
 
   /* read protos for each class into a separate list */
   while ((end_offset < 0 || ftell(File) < end_offset) &&
          tfscanf(File, "%s %d", unichar, &NumProtos) == 2) {
     if (unicharset.contains_unichar(unichar)) {
       unichar_id = unicharset.unichar_to_id(unichar);
       Protos = NormProtos->Protos[unichar_id];
       for (i = 0; i < NumProtos; i++)
         Protos =
             push_last (Protos, ReadPrototype (File, NormProtos->NumParams));
       NormProtos->Protos[unichar_id] = Protos;
     } else {
       cprintf("Error: unichar %s in normproto file is not in unichar set.\n",
               unichar);
       for (i = 0; i < NumProtos; i++)
         FreePrototype(ReadPrototype (File, NormProtos->NumParams));
     }
     SkipNewline(File);
   }
   return (NormProtos);
 }                                /* ReadNormProtos */

◆ RefreshDebugWindow()

void tesseract::Classify::RefreshDebugWindow	(	ScrollView **	win,
		const char *	msg,
		int	y_offset,
		const TBOX &	wbox
	)

Definition at line 220 of file adaptmatch.cpp.

                                                                   {
   #ifndef GRAPHICS_DISABLED
   const int kSampleSpaceWidth = 500;
   if (*win == NULL) {
     *win = new ScrollView(msg, 100, y_offset, kSampleSpaceWidth * 2, 200,
                           kSampleSpaceWidth * 2, 200, true);
   }
   (*win)->Clear();
   (*win)->Pen(64, 64, 64);
   (*win)->Line(-kSampleSpaceWidth, kBlnBaselineOffset,
                kSampleSpaceWidth, kBlnBaselineOffset);
   (*win)->Line(-kSampleSpaceWidth, kBlnXHeight + kBlnBaselineOffset,
                kSampleSpaceWidth, kBlnXHeight + kBlnBaselineOffset);
   (*win)->ZoomToRectangle(wbox.left(), wbox.top(),
                           wbox.right(), wbox.bottom());
   #endif  // GRAPHICS_DISABLED
 }

◆ RemoveBadMatches()

void tesseract::Classify::RemoveBadMatches ( ADAPT_RESULTS * Results )

This routine steps through each matching class in Results and removes it from the match list if its rating is worse than the BestRating plus a pad. In other words, all good matches get moved to the front of the classes array.

Parameters

Results contains matches to be filtered

Globals:

matcher_bad_match_pad defines a "bad match"

Note: Exceptions: none; History: Tue Mar 12 13:51:03 1991, DSJ, Created.

Definition at line 2100 of file adaptmatch.cpp.

                                                       {
   int Next, NextGood;
   FLOAT32 BadMatchThreshold;
   static const char* romans = "i v x I V X";
   BadMatchThreshold = Results->best_rating - matcher_bad_match_pad;
 
   if (classify_bln_numeric_mode) {
     UNICHAR_ID unichar_id_one = unicharset.contains_unichar("1") ?
         unicharset.unichar_to_id("1") : -1;
     UNICHAR_ID unichar_id_zero = unicharset.contains_unichar("0") ?
         unicharset.unichar_to_id("0") : -1;
     float scored_one = ScoredUnichar(unichar_id_one, *Results);
     float scored_zero = ScoredUnichar(unichar_id_zero, *Results);
 
     for (Next = NextGood = 0; Next < Results->match.size(); Next++) {
       const UnicharRating& match = Results->match[Next];
       if (match.rating >= BadMatchThreshold) {
         if (!unicharset.get_isalpha(match.unichar_id) ||
             strstr(romans,
                    unicharset.id_to_unichar(match.unichar_id)) != NULL) {
         } else if (unicharset.eq(match.unichar_id, "l") &&
                    scored_one < BadMatchThreshold) {
           Results->match[Next].unichar_id = unichar_id_one;
         } else if (unicharset.eq(match.unichar_id, "O") &&
                    scored_zero < BadMatchThreshold) {
           Results->match[Next].unichar_id = unichar_id_zero;
         } else {
           Results->match[Next].unichar_id = INVALID_UNICHAR_ID;  // Don't copy.
         }
         if (Results->match[Next].unichar_id != INVALID_UNICHAR_ID) {
           if (NextGood == Next) {
             ++NextGood;
           } else {
             Results->match[NextGood++] = Results->match[Next];
           }
         }
       }
     }
   } else {
     for (Next = NextGood = 0; Next < Results->match.size(); Next++) {
       if (Results->match[Next].rating >= BadMatchThreshold) {
         if (NextGood == Next) {
           ++NextGood;
         } else {
           Results->match[NextGood++] = Results->match[Next];
         }
       }
     }
   }
   Results->match.truncate(NextGood);
 }                              /* RemoveBadMatches */

◆ RemoveExtraPuncs()

void tesseract::Classify::RemoveExtraPuncs ( ADAPT_RESULTS * Results )

This routine discards extra digits or punctuation from the results. We keep only the top 2 punctuation answers and the top 1 digit answer if present.

Parameters

Results contains matches to be filtered

Note: History: Tue Mar 12 13:51:03 1991, DSJ, Created.

Definition at line 2162 of file adaptmatch.cpp.

                                                       {
   int Next, NextGood;
   int punc_count;              /*no of garbage characters */
   int digit_count;
   /*garbage characters */
   static char punc_chars[] = ". , ; : / ` ~ ' - = \\ | \" ! _ ^";
   static char digit_chars[] = "0 1 2 3 4 5 6 7 8 9";
 
   punc_count = 0;
   digit_count = 0;
   for (Next = NextGood = 0; Next < Results->match.size(); Next++) {
     const UnicharRating& match = Results->match[Next];
     bool keep = true;
     if (strstr(punc_chars,
                unicharset.id_to_unichar(match.unichar_id)) != NULL) {
       if (punc_count >= 2)
         keep = false;
       punc_count++;
     } else {
       if (strstr(digit_chars,
                  unicharset.id_to_unichar(match.unichar_id)) != NULL) {
         if (digit_count >= 1)
           keep = false;
         digit_count++;
       }
     }
     if (keep) {
       if (NextGood == Next) {
         ++NextGood;
       } else {
         Results->match[NextGood++] = match;
       }
     }
   }
   Results->match.truncate(NextGood);
 }                              /* RemoveExtraPuncs */

◆ ResetAdaptiveClassifierInternal()

void tesseract::Classify::ResetAdaptiveClassifierInternal ( )

Definition at line 613 of file adaptmatch.cpp.

                                                {
   if (classify_learning_debug_level > 0) {
     tprintf("Resetting adaptive classifier (NumAdaptationsFailed=%d)\n",
             NumAdaptationsFailed);
   }
   free_adapted_templates(AdaptedTemplates);
   AdaptedTemplates = NewAdaptedTemplates(true);
   if (BackupAdaptedTemplates != NULL)
     free_adapted_templates(BackupAdaptedTemplates);
   BackupAdaptedTemplates = NULL;
   NumAdaptationsFailed = 0;
 }

◆ SetAdaptiveThreshold()

void tesseract::Classify::SetAdaptiveThreshold ( FLOAT32 Threshold )

This routine resets the internal thresholds inside the integer matcher to correspond to the specified threshold.

Parameters

Threshold threshold for creating new templates

Globals:

matcher_good_threshold default good match rating

Note: Exceptions: none; History: Tue Apr 9 08:33:13 1991, DSJ, Created.

Definition at line 2213 of file adaptmatch.cpp.

                                                      {
   Threshold = (Threshold == matcher_good_threshold) ? 0.9: (1.0 - Threshold);
   classify_adapt_proto_threshold.set_value(
       ClipToRange<int>(255 * Threshold, 0, 255));
   classify_adapt_feature_threshold.set_value(
       ClipToRange<int>(255 * Threshold, 0, 255));
 }                              /* SetAdaptiveThreshold */

◆ SetStaticClassifier()

void tesseract::Classify::SetStaticClassifier ( ShapeClassifier * static_classifier )

Definition at line 204 of file classify.cpp.

                                                                      {
   delete static_classifier_;
   static_classifier_ = static_classifier;
 }

◆ SettupPass1()

void tesseract::Classify::SettupPass1 ( )

This routine prepares the adaptive matcher for the start of the first pass. Learning is enabled (unless it is disabled for the whole program).

Note: this is somewhat redundant, it simply says that if learning is enabled then it will remain enabled on the first pass. If it is disabled, then it will remain disabled. This is only put here to make it very clear that learning is controlled directly by the global setting of EnableLearning.

Globals:

EnableLearning set to TRUE by this routine

Note: Exceptions: none; History: Mon Apr 15 16:39:29 1991, DSJ, Created.

Definition at line 670 of file adaptmatch.cpp.

                            {
   EnableLearning = classify_enable_learning;
 
   getDict().SettupStopperPass1();
 
 }                                /* SettupPass1 */

◆ SettupPass2()

void tesseract::Classify::SettupPass2 ( )

This routine prepares the adaptive matcher for the start of the second pass. Further learning is disabled.

Globals:

EnableLearning set to FALSE by this routine

Note: Exceptions: none; History: Mon Apr 15 16:39:29 1991, DSJ, Created.

Definition at line 690 of file adaptmatch.cpp.

                            {
   EnableLearning = FALSE;
   getDict().SettupStopperPass2();
 
 }                                /* SettupPass2 */

◆ SetupBLCNDenorms()

void tesseract::Classify::SetupBLCNDenorms	(	const TBLOB &	blob,
		bool	nonlinear_norm,
		DENORM *	bl_denorm,
		DENORM *	cn_denorm,
		INT_FX_RESULT_STRUCT *	fx_info
	)

static

Definition at line 133 of file intfx.cpp.

                                                                {
   // Compute 1st and 2nd moments of the original outline.
   FCOORD center, second_moments;
   int length = blob.ComputeMoments(&center, &second_moments);
   if (fx_info != NULL) {
     fx_info->Length = length;
     fx_info->Rx = IntCastRounded(second_moments.y());
     fx_info->Ry = IntCastRounded(second_moments.x());
 
     fx_info->Xmean = IntCastRounded(center.x());
     fx_info->Ymean = IntCastRounded(center.y());
   }
   // Setup the denorm for Baseline normalization.
   bl_denorm->SetupNormalization(NULL, NULL, &blob.denorm(), center.x(), 128.0f,
                                 1.0f, 1.0f, 128.0f, 128.0f);
   // Setup the denorm for character normalization.
   if (nonlinear_norm) {
     GenericVector<GenericVector<int> > x_coords;
     GenericVector<GenericVector<int> > y_coords;
     TBOX box;
     blob.GetPreciseBoundingBox(&box);
     box.pad(1, 1);
     blob.GetEdgeCoords(box, &x_coords, &y_coords);
     cn_denorm->SetupNonLinear(&blob.denorm(), box, MAX_UINT8, MAX_UINT8,
                               0.0f, 0.0f, x_coords, y_coords);
   } else {
     cn_denorm->SetupNormalization(NULL, NULL, &blob.denorm(),
                                   center.x(), center.y(),
                                   51.2f / second_moments.x(),
                                   51.2f / second_moments.y(),
                                   128.0f, 128.0f);
   }
 }

◆ shape_table()

const ShapeTable* tesseract::Classify::shape_table ( ) const

inline

Definition at line 69 of file classify.h.

                                         {
     return shape_table_;
   }

◆ ShapeIDToClassID()

int tesseract::Classify::ShapeIDToClassID ( int shape_id ) const

Definition at line 2297 of file adaptmatch.cpp.

                                                  {
   for (int id = 0; id < PreTrainedTemplates->NumClasses; ++id) {
     int font_set_id = PreTrainedTemplates->Class[id]->font_set_id;
     ASSERT_HOST(font_set_id >= 0);
     const FontSet &fs = fontset_table_.get(font_set_id);
     for (int config = 0; config < fs.size; ++config) {
       if (fs.configs[config] == shape_id)
         return id;
     }
   }
   tprintf("Shape %d not found\n", shape_id);
   return -1;
 }

◆ ShowBestMatchFor()

void tesseract::Classify::ShowBestMatchFor	(	int	shape_id,
		const INT_FEATURE_STRUCT *	features,
		int	num_features
	)

This routine displays debug information for the best config of the given shape_id for the given set of features.

Parameters

shape_id	classifier id to work with
features	features of the unknown character
num_features	Number of features in the features array.

Note: Exceptions: none; History: Fri Mar 22 08:43:52 1991, DSJ, Created.

Definition at line 2234 of file adaptmatch.cpp.

                                                   {
 #ifndef GRAPHICS_DISABLED
   uinT32 config_mask;
   if (UnusedClassIdIn(PreTrainedTemplates, shape_id)) {
     tprintf("No built-in templates for class/shape %d\n", shape_id);
     return;
   }
   if (num_features <= 0) {
     tprintf("Illegal blob (char norm features)!\n");
     return;
   }
   UnicharRating cn_result;
   classify_norm_method.set_value(character);
   im_.Match(ClassForClassId(PreTrainedTemplates, shape_id),
             AllProtosOn, AllConfigsOn,
             num_features, features, &cn_result,
             classify_adapt_feature_threshold, NO_DEBUG,
             matcher_debug_separate_windows);
   tprintf("\n");
   config_mask = 1 << cn_result.config;
 
   tprintf("Static Shape ID: %d\n", shape_id);
   ShowMatchDisplay();
   im_.Match(ClassForClassId(PreTrainedTemplates, shape_id),
             AllProtosOn, reinterpret_cast<BIT_VECTOR>(&config_mask),
             num_features, features, &cn_result,
             classify_adapt_feature_threshold,
             matcher_debug_flags,
             matcher_debug_separate_windows);
   UpdateMatchDisplay();
 #endif  // GRAPHICS_DISABLED
 }                              /* ShowBestMatchFor */

◆ ShowMatchDisplay()

void tesseract::Classify::ShowMatchDisplay ( )

This routine sends the shapes in the global display lists to the match debugger window.

Globals:

FeatureShapes display list containing feature matches
ProtoShapes display list containing proto matches
Returns
none

Note
Exceptions: none

History: Thu Mar 21 15:47:33 1991, DSJ, Created.

Definition at line 1070 of file intproto.cpp.

                                 {
   InitIntMatchWindowIfReqd();
   if (ProtoDisplayWindow) {
     ProtoDisplayWindow->Clear();
   }
   if (FeatureDisplayWindow) {
     FeatureDisplayWindow->Clear();
   }
   ClearFeatureSpaceWindow(
       static_cast<NORM_METHOD>(static_cast<int>(classify_norm_method)),
       IntMatchWindow);
   IntMatchWindow->ZoomToRectangle(INT_MIN_X, INT_MIN_Y,
                                   INT_MAX_X, INT_MAX_Y);
   if (ProtoDisplayWindow) {
     ProtoDisplayWindow->ZoomToRectangle(INT_MIN_X, INT_MIN_Y,
                                         INT_MAX_X, INT_MAX_Y);
   }
   if (FeatureDisplayWindow) {
     FeatureDisplayWindow->ZoomToRectangle(INT_MIN_X, INT_MIN_Y,
                                           INT_MAX_X, INT_MAX_Y);
   }
 }                                /* ShowMatchDisplay */

◆ StartBackupAdaptiveClassifier()

void tesseract::Classify::StartBackupAdaptiveClassifier ( )

Definition at line 644 of file adaptmatch.cpp.

                                              {
   if (BackupAdaptedTemplates != NULL)
     free_adapted_templates(BackupAdaptedTemplates);
   BackupAdaptedTemplates = NewAdaptedTemplates(true);
 }

◆ SwitchAdaptiveClassifier()

void tesseract::Classify::SwitchAdaptiveClassifier ( )

Definition at line 628 of file adaptmatch.cpp.

                                         {
   if (BackupAdaptedTemplates == NULL) {
     ResetAdaptiveClassifierInternal();
     return;
   }
   if (classify_learning_debug_level > 0) {
     tprintf("Switch to backup adaptive classifier (NumAdaptationsFailed=%d)\n",
             NumAdaptationsFailed);
   }
   free_adapted_templates(AdaptedTemplates);
   AdaptedTemplates = BackupAdaptedTemplates;
   BackupAdaptedTemplates = NULL;
   NumAdaptationsFailed = 0;
 }

◆ TempConfigReliable()

bool tesseract::Classify::TempConfigReliable	(	CLASS_ID	class_id,
		const TEMP_CONFIG &	config
	)

Definition at line 2313 of file adaptmatch.cpp.

                                                              {
   if (classify_learning_debug_level >= 1) {
     tprintf("NumTimesSeen for config of %s is %d\n",
             getDict().getUnicharset().debug_str(class_id).string(),
             config->NumTimesSeen);
   }
   if (config->NumTimesSeen >= matcher_sufficient_examples_for_prototyping) {
     return true;
   } else if (config->NumTimesSeen < matcher_min_examples_for_prototyping) {
     return false;
   } else if (use_ambigs_for_adaption) {
     // Go through the ambigs vector and see whether we have already seen
     // enough times all the characters represented by the ambigs vector.
     const UnicharIdVector *ambigs =
       getDict().getUnicharAmbigs().AmbigsForAdaption(class_id);
     int ambigs_size = (ambigs == NULL) ? 0 : ambigs->size();
     for (int ambig = 0; ambig < ambigs_size; ++ambig) {
       ADAPT_CLASS ambig_class = AdaptedTemplates->Class[(*ambigs)[ambig]];
       assert(ambig_class != NULL);
       if (ambig_class->NumPermConfigs == 0 &&
           ambig_class->MaxNumTimesSeen <
           matcher_min_examples_for_prototyping) {
         if (classify_learning_debug_level >= 1) {
           tprintf("Ambig %s has not been seen enough times,"
                   " not making config for %s permanent\n",
                   getDict().getUnicharset().debug_str(
                       (*ambigs)[ambig]).string(),
                   getDict().getUnicharset().debug_str(class_id).string());
         }
         return false;
       }
     }
   }
   return true;
 }

◆ UpdateAmbigsGroup()

void tesseract::Classify::UpdateAmbigsGroup	(	CLASS_ID	class_id,
		TBLOB *	Blob
	)

Definition at line 2350 of file adaptmatch.cpp.

                                                                {
   const UnicharIdVector *ambigs =
     getDict().getUnicharAmbigs().ReverseAmbigsForAdaption(class_id);
   int ambigs_size = (ambigs == NULL) ? 0 : ambigs->size();
   if (classify_learning_debug_level >= 1) {
     tprintf("Running UpdateAmbigsGroup for %s class_id=%d\n",
             getDict().getUnicharset().debug_str(class_id).string(), class_id);
   }
   for (int ambig = 0; ambig < ambigs_size; ++ambig) {
     CLASS_ID ambig_class_id = (*ambigs)[ambig];
     const ADAPT_CLASS ambigs_class = AdaptedTemplates->Class[ambig_class_id];
     for (int cfg = 0; cfg < MAX_NUM_CONFIGS; ++cfg) {
       if (ConfigIsPermanent(ambigs_class, cfg)) continue;
       const TEMP_CONFIG config =
         TempConfigFor(AdaptedTemplates->Class[ambig_class_id], cfg);
       if (config != NULL && TempConfigReliable(ambig_class_id, config)) {
         if (classify_learning_debug_level >= 1) {
           tprintf("Making config %d of %s permanent\n", cfg,
                   getDict().getUnicharset().debug_str(
                       ambig_class_id).string());
         }
         MakePermanent(AdaptedTemplates, ambig_class_id, cfg, Blob);
       }
     }
   }
 }

◆ WriteAdaptedTemplates()

void tesseract::Classify::WriteAdaptedTemplates	(	FILE *	File,
		ADAPT_TEMPLATES	Templates
	)

This routine saves Templates to File in a binary format.

Parameters

File	open text file to write Templates to
Templates	set of adapted templates to write to File

Note: Globals: none; Exceptions: none; History: Mon Mar 18 15:07:32 1991, DSJ, Created.

Definition at line 505 of file adaptive.cpp.

                                                                           {
   int i;
 
   /* first write the high level adaptive template struct */
   fwrite ((char *) Templates, sizeof (ADAPT_TEMPLATES_STRUCT), 1, File);
 
   /* then write out the basic integer templates */
   WriteIntTemplates (File, Templates->Templates, unicharset);
 
   /* then write out the adaptive info for each class */
   for (i = 0; i < (Templates->Templates)->NumClasses; i++) {
     WriteAdaptedClass (File, Templates->Class[i],
       Templates->Templates->Class[i]->NumConfigs);
   }
 }                                /* WriteAdaptedTemplates */

◆ WriteIntTemplates()

void tesseract::Classify::WriteIntTemplates	(	FILE *	File,
		INT_TEMPLATES	Templates,
		const UNICHARSET &	target_unicharset
	)

This routine writes Templates to File. The format is an efficient binary format. File must already be open for writing.

Parameters

File	open file to write templates to
Templates	templates to save into File
target_unicharset	the UNICHARSET to use

Returns: none

Note: Globals: none; Exceptions: none; History: Wed Feb 27 11:48:46 1991, DSJ, Created.

Definition at line 1129 of file intproto.cpp.

                                                                       {
   int i, j;
   INT_CLASS Class;
   int unicharset_size = target_unicharset.size();
   int version_id = -5;  // When negated by the reader -1 becomes +1 etc.
 
   if (Templates->NumClasses != unicharset_size) {
     cprintf("Warning: executing WriteIntTemplates() with %d classes in"
             " Templates, while target_unicharset size is %d\n",
             Templates->NumClasses, unicharset_size);
   }
 
   /* first write the high level template struct */
   fwrite(&unicharset_size, sizeof(unicharset_size), 1, File);
   fwrite(&version_id, sizeof(version_id), 1, File);
   fwrite(&Templates->NumClassPruners, sizeof(Templates->NumClassPruners),
          1, File);
   fwrite(&Templates->NumClasses, sizeof(Templates->NumClasses), 1, File);
 
   /* then write out the class pruners */
   for (i = 0; i < Templates->NumClassPruners; i++)
     fwrite(Templates->ClassPruners[i],
            sizeof(CLASS_PRUNER_STRUCT), 1, File);
 
   /* then write out each class */
   for (i = 0; i < Templates->NumClasses; i++) {
     Class = Templates->Class[i];
 
     /* first write out the high level struct for the class */
     fwrite(&Class->NumProtos, sizeof(Class->NumProtos), 1, File);
     fwrite(&Class->NumProtoSets, sizeof(Class->NumProtoSets), 1, File);
     ASSERT_HOST(Class->NumConfigs == this->fontset_table_.get(Class->font_set_id).size);
     fwrite(&Class->NumConfigs, sizeof(Class->NumConfigs), 1, File);
     for (j = 0; j < Class->NumConfigs; ++j) {
       fwrite(&Class->ConfigLengths[j], sizeof(uinT16), 1, File);
     }
 
     /* then write out the proto lengths */
     if (MaxNumIntProtosIn (Class) > 0) {
       fwrite ((char *) (Class->ProtoLengths), sizeof (uinT8),
               MaxNumIntProtosIn (Class), File);
     }
 
     /* then write out the proto sets */
     for (j = 0; j < Class->NumProtoSets; j++)
       fwrite ((char *) Class->ProtoSets[j],
               sizeof (PROTO_SET_STRUCT), 1, File);
 
     /* then write the fonts info */
     fwrite(&Class->font_set_id, sizeof(int), 1, File);
   }
 
   /* Write the fonts info tables */
   this->fontinfo_table_.write(File, NewPermanentTessCallback(write_info));
   this->fontinfo_table_.write(File,
                               NewPermanentTessCallback(write_spacing_info));
   this->fontset_table_.write(File, NewPermanentTessCallback(write_set));
 }                                /* WriteIntTemplates */

◆ WriteTRFile()

bool tesseract::Classify::WriteTRFile ( const STRING & filename )

Definition at line 97 of file blobclass.cpp.

                                                  {
   STRING tr_filename = filename + ".tr";
   FILE* fp = Efopen(tr_filename.string(), "wb");
   int len = tr_file_data_.length();
   bool result =
       fwrite(&tr_file_data_[0], sizeof(tr_file_data_[0]), len, fp) == len;
   fclose(fp);
   tr_file_data_.truncate_at(0);
   return result;
 }

Member Data Documentation

◆ AdaptedTemplates

ADAPT_TEMPLATES tesseract::Classify::AdaptedTemplates

Definition at line 473 of file classify.h.

◆ AllConfigsOff

BIT_VECTOR tesseract::Classify::AllConfigsOff

Definition at line 482 of file classify.h.

◆ AllConfigsOn

BIT_VECTOR tesseract::Classify::AllConfigsOn

Definition at line 481 of file classify.h.

◆ allow_blob_division

bool tesseract::Classify::allow_blob_division = true

"Use divisible blobs chopping"

Definition at line 382 of file classify.h.

◆ AllProtosOn

BIT_VECTOR tesseract::Classify::AllProtosOn

Definition at line 480 of file classify.h.

◆ BackupAdaptedTemplates

ADAPT_TEMPLATES tesseract::Classify::BackupAdaptedTemplates

Definition at line 477 of file classify.h.

◆ certainty_scale

double tesseract::Classify::certainty_scale = 20.0

"Certainty scaling factor"

Definition at line 437 of file classify.h.

◆ classify_adapt_feature_threshold

int tesseract::Classify::classify_adapt_feature_threshold = 230

"Threshold for good features during adaptive 0-255"

Definition at line 447 of file classify.h.

◆ classify_adapt_proto_threshold

int tesseract::Classify::classify_adapt_proto_threshold = 230

"Threshold for good protos during adaptive 0-255"

Definition at line 445 of file classify.h.

◆ classify_adapted_pruning_factor

double tesseract::Classify::classify_adapted_pruning_factor = 2.5

"Prune poor adapted results this much worse than best result"

Definition at line 441 of file classify.h.

◆ classify_adapted_pruning_threshold

double tesseract::Classify::classify_adapted_pruning_threshold = -1.0

"Threshold at which classify_adapted_pruning_factor starts"

Definition at line 443 of file classify.h.

◆ classify_bln_numeric_mode

bool tesseract::Classify::classify_bln_numeric_mode = 0

"Assume the input is numbers [0-9]."

Definition at line 500 of file classify.h.

◆ classify_char_norm_range

double tesseract::Classify::classify_char_norm_range = 0.2

"Character Normalization Range ..."

Definition at line 396 of file classify.h.

◆ classify_character_fragments_garbage_certainty_threshold

double tesseract::Classify::classify_character_fragments_garbage_certainty_threshold = -3.0

"Exclude fragments that do not match any whole character" " with at least this certainty"

Definition at line 453 of file classify.h.

◆ classify_class_pruner_multiplier

int tesseract::Classify::classify_class_pruner_multiplier = 15

"Class Pruner Multiplier 0-255: "

Definition at line 465 of file classify.h.

◆ classify_class_pruner_threshold

int tesseract::Classify::classify_class_pruner_threshold = 229

"Class Pruner Threshold 0-255"

Definition at line 463 of file classify.h.

◆ classify_cp_cutoff_strength

int tesseract::Classify::classify_cp_cutoff_strength = 7

"Class Pruner CutoffStrength: "

Definition at line 467 of file classify.h.

◆ classify_debug_character_fragments

bool tesseract::Classify::classify_debug_character_fragments = FALSE

"Bring up graphical debugging windows for fragments training"

Definition at line 455 of file classify.h.

◆ classify_debug_level

int tesseract::Classify::classify_debug_level = 0

"Classify debug level"

Definition at line 390 of file classify.h.

◆ classify_enable_adaptive_debugger

bool tesseract::Classify::classify_enable_adaptive_debugger = 0

"Enable match debugger"

Definition at line 414 of file classify.h.

◆ classify_enable_adaptive_matcher

bool tesseract::Classify::classify_enable_adaptive_matcher = 1

"Enable adaptive classifier"

Definition at line 409 of file classify.h.

◆ classify_enable_learning

bool tesseract::Classify::classify_enable_learning = true

"Enable adaptive classifier"

Definition at line 389 of file classify.h.

◆ classify_integer_matcher_multiplier

int tesseract::Classify::classify_integer_matcher_multiplier = 10

"Integer Matcher Multiplier 0-255: "

Definition at line 469 of file classify.h.

◆ classify_learn_debug_str

char* tesseract::Classify::classify_learn_debug_str = ""

"Class str to debug learning"

Definition at line 459 of file classify.h.

◆ classify_learning_debug_level

int tesseract::Classify::classify_learning_debug_level = 0

"Learning Debug Level: "

Definition at line 419 of file classify.h.

◆ classify_max_certainty_margin

double tesseract::Classify::classify_max_certainty_margin = 5.5

"Veto difference between classifier certainties"

Definition at line 404 of file classify.h.

◆ classify_max_norm_scale_x

double tesseract::Classify::classify_max_norm_scale_x = 0.325

"Max char x-norm scale ..."

Definition at line 398 of file classify.h.

◆ classify_max_norm_scale_y

double tesseract::Classify::classify_max_norm_scale_y = 0.325

"Max char y-norm scale ..."

Definition at line 400 of file classify.h.

◆ classify_max_rating_ratio

double tesseract::Classify::classify_max_rating_ratio = 1.5

"Veto ratio between classifier ratings"

Definition at line 402 of file classify.h.

◆ classify_min_norm_scale_x

double tesseract::Classify::classify_min_norm_scale_x = 0.0

"Min char x-norm scale ..."

Definition at line 397 of file classify.h.

◆ classify_min_norm_scale_y

double tesseract::Classify::classify_min_norm_scale_y = 0.0

"Min char y-norm scale ..."

Definition at line 399 of file classify.h.

◆ classify_misfit_junk_penalty

double tesseract::Classify::classify_misfit_junk_penalty = 0.0

"Penalty to apply when a non-alnum is vertically out of " "its expected textline position"

Definition at line 435 of file classify.h.

◆ classify_nonlinear_norm

bool tesseract::Classify::classify_nonlinear_norm = 0

"Non-linear stroke-density normalization"

Definition at line 416 of file classify.h.

◆ classify_norm_method

int tesseract::Classify::classify_norm_method = character

"Normalization Method ..."

Definition at line 394 of file classify.h.

◆ classify_save_adapted_templates

bool tesseract::Classify::classify_save_adapted_templates = 0

"Save adapted templates to a file"

Definition at line 413 of file classify.h.

◆ classify_use_pre_adapted_templates

bool tesseract::Classify::classify_use_pre_adapted_templates = 0

"Use pre-adapted classifier templates"

Definition at line 411 of file classify.h.

◆ disable_character_fragments

bool tesseract::Classify::disable_character_fragments = TRUE

"Do not include character fragments in the" " results of the classifier"

Definition at line 450 of file classify.h.

◆ EnableLearning

bool tesseract::Classify::EnableLearning

Definition at line 484 of file classify.h.

◆ feature_defs_

FEATURE_DEFS_STRUCT tesseract::Classify::feature_defs_

protected

Definition at line 507 of file classify.h.

◆ fontinfo_table_

UnicityTable<FontInfo> tesseract::Classify::fontinfo_table_

Definition at line 488 of file classify.h.

◆ fontset_table_

UnicityTable<FontSet> tesseract::Classify::fontset_table_

Definition at line 496 of file classify.h.

◆ il1_adaption_test

int tesseract::Classify::il1_adaption_test = 0

"Don't adapt to i/I at beginning of word"

Definition at line 498 of file classify.h.

◆ im_

IntegerMatcher tesseract::Classify::im_

protected

Definition at line 503 of file classify.h.

◆ matcher_avg_noise_size

double tesseract::Classify::matcher_avg_noise_size = 12.0

"Avg. noise blob length: "

Definition at line 425 of file classify.h.

◆ matcher_bad_match_pad

double tesseract::Classify::matcher_bad_match_pad = 0.15

"Bad Match Pad (0-1)"

Definition at line 423 of file classify.h.

◆ matcher_clustering_max_angle_delta

double tesseract::Classify::matcher_clustering_max_angle_delta = 0.015

"Maximum angle delta for prototype clustering"

Definition at line 432 of file classify.h.

◆ matcher_debug_flags

int tesseract::Classify::matcher_debug_flags = 0

"Matcher Debug Flags"

Definition at line 418 of file classify.h.

◆ matcher_debug_level

int tesseract::Classify::matcher_debug_level = 0

"Matcher Debug Level"

Definition at line 417 of file classify.h.

◆ matcher_debug_separate_windows

bool tesseract::Classify::matcher_debug_separate_windows = FALSE

"Use two different windows for debugging the matching: " "One for the protos and one for the features."

Definition at line 458 of file classify.h.

◆ matcher_good_threshold

double tesseract::Classify::matcher_good_threshold = 0.125

"Good Match (0-1)"

Definition at line 420 of file classify.h.

◆ matcher_min_examples_for_prototyping

int tesseract::Classify::matcher_min_examples_for_prototyping = 3

"Reliable Config Threshold"

Definition at line 428 of file classify.h.

◆ matcher_perfect_threshold

double tesseract::Classify::matcher_perfect_threshold = 0.02

"Perfect Match (0-1)"

Definition at line 422 of file classify.h.

◆ matcher_permanent_classes_min

int tesseract::Classify::matcher_permanent_classes_min = 1

"Min # of permanent classes"

Definition at line 426 of file classify.h.

◆ matcher_rating_margin

double tesseract::Classify::matcher_rating_margin = 0.1

"New template margin (0-1)"

Definition at line 424 of file classify.h.

◆ matcher_reliable_adaptive_result

double tesseract::Classify::matcher_reliable_adaptive_result = 0.0

"Great Match (0-1)"

Definition at line 421 of file classify.h.

◆ matcher_sufficient_examples_for_prototyping

int tesseract::Classify::matcher_sufficient_examples_for_prototyping = 5

"Enable adaption even if the ambiguities have not been seen"

Definition at line 430 of file classify.h.

◆ NormProtos

NORM_PROTOS* tesseract::Classify::NormProtos

Definition at line 486 of file classify.h.

◆ PreTrainedTemplates

INT_TEMPLATES tesseract::Classify::PreTrainedTemplates

Definition at line 469 of file classify.h.

◆ prioritize_division

bool tesseract::Classify::prioritize_division = FALSE

"Prioritize blob division over chopping"

Definition at line 387 of file classify.h.

◆ rating_scale

double tesseract::Classify::rating_scale = 1.5

"Rating scaling factor"

Definition at line 436 of file classify.h.

◆ shape_table_

ShapeTable* tesseract::Classify::shape_table_

protected

Definition at line 512 of file classify.h.

◆ speckle_large_max_size

double tesseract::Classify::speckle_large_max_size = 0.30

"Max large speckle size"

Definition at line 501 of file classify.h.

◆ speckle_rating_penalty

double tesseract::Classify::speckle_rating_penalty = 10.0

"Penalty to add to worst rating for noise"

Definition at line 503 of file classify.h.

◆ TempProtoMask

BIT_VECTOR tesseract::Classify::TempProtoMask

Definition at line 483 of file classify.h.

◆ tess_bn_matching

bool tesseract::Classify::tess_bn_matching = 0

"Baseline Normalized Matching"

Definition at line 408 of file classify.h.

◆ tess_cn_matching

bool tesseract::Classify::tess_cn_matching = 0

"Character Normalized Matching"

Definition at line 407 of file classify.h.

◆ tessedit_class_miss_scale

double tesseract::Classify::tessedit_class_miss_scale = 0.00390625

"Scale factor for features not used"

Definition at line 439 of file classify.h.

◆ tessedit_single_match

int tesseract::Classify::tessedit_single_match = FALSE

"Top choice only from CP"

Definition at line 388 of file classify.h.

The documentation for this class was generated from the following files:

classify/classify.h
classify/adaptive.cpp
classify/adaptmatch.cpp
classify/blobclass.cpp
classify/classify.cpp
classify/cutoffs.cpp
classify/float2int.cpp
classify/intfx.cpp
classify/intmatcher.cpp
classify/intproto.cpp
classify/mfoutline.cpp
classify/normmatch.cpp
classify/outfeat.cpp
classify/picofeat.cpp

Outlines	list of outlines to be normalized
XScale	x-direction scale factor used by routine
YScale	y-direction scale factor used by routine

Public Member Functions

Static Public Member Functions

Public Attributes

Protected Attributes

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ Classify()

◆ ~Classify()

Member Function Documentation

◆ AdaptableWord()

◆ AdaptiveClassifier()

◆ AdaptiveClassifierIsEmpty()

◆ AdaptiveClassifierIsFull()

◆ AdaptToChar()

◆ AddLargeSpeckleTo()

◆ AddNewResult()

◆ AmbigClassifier()

◆ BaselineClassifier()

◆ CharNormClassifier()

◆ CharNormTrainingSample()

◆ ClassAndConfigIDToFontOrShapeID()

◆ ClassIDToDebugStr()

◆ ClassifyAsNoise()

◆ ClearCharNormArray()

◆ ComputeCharNormArrays()

◆ ComputeCorrectedRating()

◆ ComputeIntCharNormArray()

◆ ComputeIntFeatures()

◆ ComputeNormMatch()

◆ ConvertMatchesToChoices()

◆ ConvertProto()

◆ CreateIntTemplates()

◆ DebugAdaptiveClassifier()

◆ DisplayAdaptedChar()

◆ DoAdaptiveMatch()

◆ EndAdaptiveClassifier()

◆ ExpandShapesAndApplyCorrections()

◆ ExtractFeatures()

◆ ExtractIntCNFeatures()

◆ ExtractIntGeoFeatures()

◆ ExtractOutlineFeatures()

◆ ExtractPicoFeatures()

◆ FreeNormProtos()

◆ get_fontinfo_table() [1/2]

◆ get_fontinfo_table() [2/2]

◆ get_fontset_table()

◆ GetAdaptiveFeatures()

◆ GetAmbiguities()

◆ GetCharNormFeature()

◆ GetClassToDebug()

◆ getDict()

◆ GetFontinfoId()

◆ InitAdaptedClass()

◆ InitAdaptiveClassifier()

◆ LargeSpeckle()

◆ LearnBlob()

◆ LearnPieces()

◆ LearnWord()

◆ LooksLikeGarbage()

◆ MakeNewTemporaryConfig()

◆ MakeNewTempProtos()

◆ MakePermanent()

◆ MasterMatcher()

◆ NewAdaptedTemplates()

◆ NormalizeOutlines()

◆ PrintAdaptedTemplates()

◆ PrintAdaptiveMatchResults()

◆ PruneClasses()

◆ ReadAdaptedTemplates()

◆ ReadIntTemplates()

◆ ReadNewCutoffs()

◆ ReadNormProtos()

◆ RefreshDebugWindow()

◆ RemoveBadMatches()

◆ RemoveExtraPuncs()

◆ ResetAdaptiveClassifierInternal()

◆ SetAdaptiveThreshold()

◆ SetStaticClassifier()

◆ SettupPass1()