openapi: 3.2.0
info:
title: OCR Features Ocr Async API
version: '2.0'
description: Your project description
servers:
- url: https://api.edenai.run/v2
tags:
- name: Ocr Async
paths:
/ocr/ocr_async/:
get:
operationId: ocr_ocr_async_retrieve
description: 'Get a list of all jobs launched for this feature. You''ll then be able to use the ID of each one to get its status and results.
Please note that a **job status doesn''t get updated until a get request** is sent.'
summary: Ocr Async List Job
tags:
- Ocr Async
security:
- FeatureApiAuth: []
- {}
responses:
'200':
content:
application/json:
schema:
$ref: '#/components/schemas/ListAsyncJobResponse'
examples:
ResponseExample:
value:
jobs:
- providers: '[''microsoft'', ''mistral'', ''amazon'']'
nb: 3
nb_ok: 3
public_id: adcbfd02-8ac8-450f-bab5-cd846b0f3f89
state: finished
created_at: '2026-09-06T03:58:32.209981'
- providers: '[''microsoft'', ''mistral'', ''amazon'']'
nb: 3
nb_ok: 3
public_id: 0741115d-e28f-4c34-95ad-d8992d432e2d
state: finished
created_at: '2026-09-06T02:58:32.209994'
- providers: '[''microsoft'', ''mistral'', ''amazon'']'
nb: 3
nb_ok: 3
public_id: ef7056e8-2745-4b33-ba6a-c801a098ae91
state: finished
created_at: '2026-09-06T01:58:32.209998'
summary: Response Example
description: ''
post:
operationId: ocr_ocr_async_create
description: 'Available Providers
|Provider|Version|Price|Billing unit|
|----|-------|-----|------------|
|**amazon**|`boto3 (v1.15.18)`|1.5 (per 1000 page)|1 page
|**microsoft**|`rest API 4.0 (2024-11-30)`|10.0 (per 1000 page)|1 page
|**mistral**|`v1`|1.0 (per 1000 page)|1 page'
summary: Ocr Async Launch Job
tags:
- Ocr Async
requestBody:
content:
multipart/form-data:
schema:
$ref: '#/components/schemas/AsyncOcrRequest'
examples:
RequestExample:
value:
providers: microsoft,mistral,amazon
file: /edenai/edenai/features/ocr/samples/data/ocr_multipages.pdf
summary: Request Example
application/json:
schema:
$ref: '#/components/schemas/AsyncOcrRequest'
examples:
RequestExample:
value:
providers: microsoft,mistral,amazon
file_url: http://edenai-resource-example.pdf
summary: Request Example
required: true
security:
- FeatureApiAuth: []
- {}
responses:
'200':
content:
application/json:
schema:
$ref: '#/components/schemas/LaunchAsyncJobResponse'
examples:
ResponseExample:
value:
public_id: 60087883-7d81-4392-b9c2-b19688e4b391
summary: Response Example
description: ''
delete:
operationId: ocr_ocr_async_destroy
description: 'Generic class to handle method GET all async job for user
Attributes:
feature (str): EdenAI feature
subfeature (str): EdenAI subfeature'
summary: Ocr Async delete Jobs
tags:
- Ocr Async
security:
- FeatureApiAuth: []
- {}
responses:
'204':
description: No response body
/ocr/ocr_async/{public_id}/:
get:
operationId: ocr_ocr_async_retrieve_2
description: Get the status and results of an async job given its ID.
summary: Ocr Async Get Job Results
parameters:
- in: path
name: public_id
schema:
type: string
required: true
- in: query
name: response_as_dict
schema:
type: boolean
default: true
- in: query
name: show_base_64
schema:
type: boolean
default: true
- in: query
name: show_original_response
schema:
type: boolean
default: false
tags:
- Ocr Async
security:
- FeatureApiAuth: []
responses:
'200':
content:
application/json:
schema:
$ref: '#/components/schemas/asyncocrocr_asyncResponseModel'
examples:
ResponseExample:
value:
public_id: aaea952e-d06f-47bd-ab99-b01debbca8db
status: finished
error: null
results:
microsoft:
error: null
id: ca1ac0e5-5b44-4340-a1bd-25cf2a5a5ac2
final_status: finished
raw_text: 'International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064
An Introduction to the Process of Optical Character Recognition
Umal Patel1 1 Department of Computer Engineering L D College Of Engineering, Gujarat Technological University, Gujarat, India
Abstract: This paper presents an overview of methods and techniques used for feature extraction that helps in efficient classification of the alphabets and numbers of English language. Character recognition has long been a essential area for research since years. Recognition of character is a minor work for humans, but to make a computer program that does character recognition is extremely difficult. Hence to make a machine recognize the characters and efficiently determine a pattern has been the primary concern for researchers now days This paper discusses various offline and online Optical Character Recognition Techniques (OCR).
Keywords: OCR, online, offline, online, zoning, euler number.
1. Introduction
OCR is an approach that provides a full alphanumeric recognition of printed or handwritten characters at electronically by simply scanning them and generating into a form that can be scanned through a scanner and then the recognition engine of the OCR system interpret the images and turn images of handwritten or printed characters into ASCII data (machine-readable characters).Character recognition also popularly referred as optical character recognition (OCR) is a field of research that has immense potential in future where we want to track and locate every piece of information being exchanged. The problem with the hand written text is due to uncertainties such as variation in calligraphy over period of time, similarity in text, variation in styles of writing [3] The character recognition system helps in making the communication between a human and a computer easy.[4] The character recognition is basically classified into two types: offline handwritten text recognition, online handwritten text recognition. Offline means the text written on the plain paper or sheet and then the writing is usually captured optically by a scanner and the completed writing is available as an image. Online means the text written on any digital devices such as tablets using stylus i.e. the two dimensional coordinates of successive points are represented as a function of time and the order of strokes made by the writer are also available.[6]
2. Applications recognition
of optical character
The area of OCR is becoming an integral part of document scanners, and is used in many applications such as postal processing, script recognition, banking, security (i.e. passport authentication) and language identification, document reading, mail sorting, signature verification, writer identification., license plate recognition system, smart card processing system, automatic data entry, bank cheque /DD processing, money counting machine, postal automation, address and zip code recognition etc many organizations are depending on OCR systems to eliminate the human interactions for better performance and efficiency [2,4,6,7].
3. Potential problem areas for OCR
1. The same characters differ in sizes, shapes and styles from person to person and even from time to time with the same person. The source of confusion is the high level of abstraction: there are thousands styles of type in common use plus variations in calligraphy and a character recognition program must recognize most of these.
2. Like any image, visual characters are subject to spoilage due to noise. Some images containing characters are already blurred or not clear which makes them difficult to process. Noise consists of random changes to a pattern, particularly near the edges. A character with much noise may be interpreted as a completely different character by a computer program.
3. There are no hard-and-fast rules that define the appearance of a visual character. Hence rules need to be heuristically deduced from the samples.
4. Phases of OCR
Data Acquisition
Pre processing
Segmentation
Normalization
Feature Extraction
Classification
Post Processing
155
Volume 2 Issue 5, May 2013 www.ijsr.net
International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064
1. Data Acquisition
Most Important initial phase in OCR is to gather the image from either device sensor like PDA or tablets in case on online recognition or getting the images containing characters directly for offline recognition.
In Image acquisition, the recognition system acquires a scanned image as an input image. The image should have a specific format such as JPEG, BMP etc. This image is acquired through a scanner, digital camera or any other suitable digital input device. Data samples for the experiment have been collected from different individuals [9].
2. Pre Processing
The goal of pre-processing is to simplify the pattern recognition problem without missing any vital information. It reduces the noises and inconsistent data. It enhances the image and prepares it for the next steps [3].
Preprocessing is the preliminary step which transforms the data into a format that will be more easily and effectively processed. Therefore, the main task in preprocessing the captured data is to decrease the variation that causes a reduction in the recognition rate and increases the complexities, as for example, preprocessing of the input raw stroke of characters is crucial for the success of efficient character recognition systems. Thus, preprocessing is an essential stage prior to feature extraction since it controls the suitability of the results for the successive stages [2].
Preprocessing can be done through various ways Binarization, Noise reduction, Stroke width normalization, Skew correction, Slant removal, Filtering, Morphological Operations, Noise Modelling, Skew Normalization, Size Normalization, Contour Smoothing, Compression, Thresholding, Thinning etc
start
start
Figure 1: Slant Removal
e :selected:
1 :selected: :selected: :selected:
Figure 2: Normalization of ''e'' and ''l'' as in [9]
3. Segmentation
Segmentation is an integral part of any text based recognition system. It assures efficiency of classification and
recognition. Accuracy of character recognition heavily depends upon segmentation phase.
payque
eighteen eighteen
eighteen
Figure 3: Segmentation [9]
4. Normalization
The results of segmentation process provides isolated characters which are ready to pass through feature extraction stage, thus the isolated characters are reduced to a specific size depending on the methods used. The segmentation process essentially renders the image in the form of m*n matrix. These matrices are then generally normalized by reducing the size and removing the redundant information from the image without losing any important information.
5. Feature Extraction
Feature extraction is the process of extracting the relevant features from objects/alphabets to form a feature vectors. These feature vectors is then used by classifiers to recognize the input unit with target output unit. It becomes easier for the classifier to classify between different classes by looking at these features as it allows fairly easy to distinguish.
Feature extraction is also defined as extracting the raw data the information which is most relevant for classification purposes in the sense of minimizing the pattern variability.[1]
Due to the nature of handwriting with its high degree of variability and imprecision obtaining these features, is a difficult task. Feature extraction methods are based on 3 types of features:
· Statistical
· Structural
. Global transformations and moments
Statistical Features includes:
1. Zoning
The character image is divided into NxM zones. From each zone features are extracted to form the feature vector. The
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Volume 2 Issue 5, May 2013 www.ijsr.net :unselected: :selected:
International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064
goal of zoning is to obtain the local characteristics instead of global characteristics.
Figure 4: zoning
After dividing the character into different zones you can compare the density or direction features of it and classify each one of them.
2. Projection Histograms
The basic idea behind using projections is that character images, which are 2-D signals, can be represented as 1- D signal. These features, although independent to noise and deformation, depend on rotation. Projection histograms count the number of pixels in each column and row of a character image. Projection histograms can separate characters such as "m" and "n".
Figure 5: Projection Histogram
3. Profiles
The profile counts the number of pixels (distance) between the bounding box of the character image and the edge of the character. The profiles describe well the external shapes of characters and allow distinguishing between a great number of letters, such as "p" and "q".
a
Figure 6: Profiling
4. Structural features:
Structural features are based on topological and geometrical properties of the character, such as aspect ratio, cross points, loops, branch points, strokes and their directions, inflection between two points, horizontal curves at top or bottom, etc.
Global Transformations-Moments:
The Fourier Transform (FT) of the contour of the image is calculated. Since the first n coefficients of the FT can be used in order to reconstruct the contour, then these n coefficients are considered to be a n-dimensional feature vector that represents the character.
00855555 $5555555 Figure 7: Contouring
6 Classification
The results Classification is the last stage where we train the neural net using the feature vectors obtained during feature extraction method against the required targets. To optimize the whole recognition process, several combination methods of multilayer perceptron have been devised. E.g .: k-Nearest Neighbour (k-NN), Bayes Classifier, Neural Networks (NN), Hidden Markov Models (HMM), Support Vector Machines (SVM), etc there is no such thing as the "best classifier". The use of classifier depends on many factors, such as available training set, number of free parameters etc.
7. Post Processing
The goal of post processing is the incorporation of context and shape information in all the stages of OCR systems is necessary for meaningful improvements in recognition rates.
5. Conclusion
The character recognition methods have been introduced and developed over the years. In this paper, I have tried to explain the overview of the whole OCR process and the methods related to it. Many researchers try to hybrid two or more different methods and compare the results for efficiency but again this will be application specific and parameter specific. OCR has been implemented in various countries for recognizing different languages as well.
References
[1] Oivind Due Trier, Anil K. Jain, Torfinn Taxt, "Feature Extraction Methods for Character Recognition-A Survey", July 1995
[2] Yasser Alginahi, Taibah University Kingdom of Saudi Arabia, "Preprocessing Techniques in Character Recognition"
[3] Om Prakash Sharma, M. K. Ghose, Krishna Bikram Shah, Benoy Kumar Thakur, "Recent Trends and Tools for Feature Extraction
[4] in OCR Technology", International Journal of Soft Computing and Engineering (IJSCE)
[5] ISSN: 2231-2307, Volume-2, Issue-6, January 2013
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International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064
[6] Suruchi G. Dedgaonkar, Anjali A. Chandavale, Ashok M. Sapkal, "Survey of Methods for Character Recognition", International Journal of Engineering and Innovative Technology (IJEIT) Volume 1, Issue 5, May 2012
[7] Mohanad Alata, Mohammad Al-Shabi "TEXT DETECTION AND CHARACTER
[8] RECOGNITION USING FUZZY IMAGE PROCESSING", Journal of ELECTRICAL ENGINEERING, VOL. 57, NO. 5, 2006, 258-267
[9] Rejean Plamondon, Fellow, IEEE and Sargur N. Shrihari, Fellow, IEEE, "On-line and Off-line Handwriting Recognition: A comprehensive Survey", IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL 22, NO. 1 JANUARY 2000
[10] Om Prakash Sharma, M. K. Ghose, Krishna Bikram Shah, "An Improved Zone Based Hybrid Feature Extraction Model for Handwritten Alphabets Recognition Using Euler Number", International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-2, Issue-2, May 2012
[11]J. Pradeepa,, E. Srinivasan, S. Himavathi, "Neural Network Based Recognition System Integrating Feature
Extraction and Classification for English Handwritten", IJE TRANSACTIONS B: Applications Vol. 25, No. 2, (May 2012) 99-106
[12] Nafiz Arica and Fatos T. Yarman-Vural, "An Overview of Character Recognition Focused on
[13] Off-Line Handwriting", IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS-PART C: APPLICATIONS AND REVIEWS, VOL. 31, NO. 2, MAY 2001
Author Profile
Umal Patel, pursuing her Master Degree in Computer Science & Technology from Gujarat Technological University (L D College of Eng., Ahmedabad), received her Bachelor Degree in Computer Engg. from Gujarat University in 2008. Presently working as Assistant Professor in Department of MCA, L.J Institute of Technology. Earlier he has served as Software Test Engineer in Lodestone Software Services since June 2008. Her area of interest is Compilers and Image Processing.
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Volume 2 Issue 5, May 2013 www.ijsr.net'
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- text: electronically by simply scanning them and generating into a
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- text: form that can be scanned through a scanner and then the
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- text: recognition engine of the OCR system interpret the images
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- text: and turn images of handwritten or printed characters into
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- text: ASCII
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- text: data
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- text: (machine-readable characters).Character
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- text: recognition also popularly referred as optical character
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- text: recognition (OCR) is a field of research that has immense
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- text: potential in future where we want to track and locate every
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- text: piece of information being exchanged. The problem with the
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- text: hand written text is due to uncertainties such as variation in
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- text: calligraphy over period of time, similarity in text, variation
words:
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- text: in styles of writing [3] The character recognition system
words:
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- text: writing
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- text: '[3]'
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- text: The
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- text: character
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- text: recognition
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- text: system
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- text: helps in making the communication between a human and a
words:
- text: helps
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- text: communication
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- text: between
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- text: human
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- text: and
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- text: computer easy.[4] The character recognition is basically
words:
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- text: easy.[4]
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- text: The
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- text: character
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- text: 'classified into two types: offline handwritten text'
words:
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- text: 'types:'
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- text: offline
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- text: handwritten
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- text: recognition, online handwritten text recognition. Offline
words:
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- text: recognition.
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- text: Offline
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- text: means the text written on the plain paper or sheet and then
words:
- text: means
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- text: the writing is usually captured optically by a scanner and the
words:
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confidence: 99.6
- text: the
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confidence: null
- text: completed writing is available as an image. Online means the
words:
- text: completed
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- text: writing
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confidence: 99.4
- text: Online
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confidence: 99.5
- text: means
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confidence: 99.5
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confidence: null
- text: text written on any digital devices such as tablets using
words:
- text: text
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confidence: 99.5
- text: tablets
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- text: stylus i.e. the two dimensional coordinates of successive
words:
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- text: depending on OCR systems to eliminate the human
words:
- text: depending
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- text: interactions for better performance and efficiency [2,4,6,7].
words:
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- text: '[2,4,6,7].'
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- text: 3. Potential problem areas for OCR
words:
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- text: Potential
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- text: 1. The same characters differ in sizes, shapes and styles
words:
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- text: from person to person and even from time to time with
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- text: from
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- text: the same person. The source of confusion is the high
words:
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- text: 'level of abstraction: there are thousands styles of type in'
words:
- text: level
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- text: 'abstraction:'
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- text: common use plus variations in calligraphy and a
words:
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- text: character recognition program must recognize most of
words:
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- text: these.
words:
- text: these.
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- text: 2. Like any image, visual characters are subject to spoilage
words:
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- text: due to noise. Some images containing characters are
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- text: character recognition systems. Thus, preprocessing is an
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- text: essential stage prior to feature extraction since it controls the
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- text: suitability of the results for the successive stages [2].
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- text: Preprocessing can be done through various
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- text: ways
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- text: Binarization, Noise reduction, Stroke width normalization,
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- text: 'Figure 2: Normalization of ''e'' and ''l'' as in [9]'
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- text: 3. Segmentation
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- text: 'Figure 3: Segmentation [9]'
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- text: 4. Normalization
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- text: from the image without losing any important information.
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- text: 5. Feature Extraction
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- text: '5.'
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- text: Feature extraction is the process of extracting the relevant
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- text: Feature
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- text: features from objects/alphabets to form a feature vectors.
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- text: features
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- text: These feature vectors is then used by classifiers to recognize
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- text: These
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- text: at these features as it allows fairly easy to distinguish.
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- text: at
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- text: Feature extraction is also defined as extracting the raw data
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- text: Feature
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- text: the information which is most relevant for classification
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- text: purposes in the sense of minimizing the pattern
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- text: variability.[1]
words:
- text: variability.[1]
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- text: Due to the nature of handwriting with its high degree of
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- text: Due
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- text: 'Figure 4: zoning'
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- text: classify each one of them.
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- text: 2. Projection Histograms
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- text: The basic idea behind using projections is that character
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- text: D signal. These features, although independent to noise
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- text: and deformation, depend on rotation. Projection
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- text: histograms count the number of pixels in each column
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- text: and row of a character image. Projection histograms can
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- text: 'Figure 5: Projection Histogram'
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- text: 3. Profiles
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width: 0.015355945739904904
height: 0.012151115063620203
confidence: 99.5
- text: Profiles
bounding_box:
left: 0.7978557339754838
top: 0.09332672048159803
width: 0.055893706361403214
height: 0.011287453824052586
confidence: 99.5
bounding_box:
left: 0.7972385919481118
top: 0.07725920098508687
width: 0.07863115478164062
height: 0.011715008893145477
confidence: null
- text: The profile counts the number of pixels (distance)
words:
- text: The
bounding_box:
left: 0.837159210542238
top: 0.07856751949651114
width: 0.027021140139643504
height: 0.013023327404569737
confidence: 99.2
- text: profile
bounding_box:
left: 0.837159210542238
top: 0.10374196196470106
width: 0.04729002045039268
height: 0.013023327404569737
confidence: 99.3
- text: counts
bounding_box:
left: 0.837159210542238
top: 0.1432394992475031
width: 0.04668497924708675
height: 0.013023327404569737
confidence: 99.4
- text: the
bounding_box:
left: 0.837159210542238
top: 0.18143726912026267
width: 0.0233424896235434
height: 0.013023327404569737
confidence: 99.6
- text: number
bounding_box:
left: 0.837159210542238
top: 0.20401217676836778
width: 0.05465942230665906
height: 0.013450882473662627
confidence: 99.4
- text: of
bounding_box:
left: 0.837159210542238
top: 0.24785367355315363
width: 0.018417454228632984
height: 0.013450882473662627
confidence: 99.5
- text: pixels
bounding_box:
left: 0.837159210542238
top: 0.26521240935832535
width: 0.044228511961664586
height: 0.013450882473662627
confidence: 98.6
- text: (distance)
bounding_box:
left: 0.837159210542238
top: 0.3016743056505678
width: 0.06757100158520793
height: 0.013886988644137354
confidence: 96.3
bounding_box:
left: 0.8365420685148659
top: 0.07769530715556164
width: 0.3839107443216883
height: 0.013459433575044465
confidence: null
- text: between the bounding box of the character image and
words:
- text: between
bounding_box:
left: 0.8574280908529871
top: 0.07725920098508687
width: 0.057127990416147335
height: 0.0117150088931454
confidence: 99.5
- text: the
bounding_box:
left: 0.8574280908529871
top: 0.12110069776987276
width: 0.02395963165091545
height: 0.012587221234095008
confidence: 99.6
- text: bounding
bounding_box:
left: 0.856810948825615
top: 0.1415036256669859
width: 0.06573167632715787
height: 0.012587221234095008
confidence: 99.5
- text: box
bounding_box:
left: 0.856810948825615
top: 0.1914249555342728
width: 0.027638282167015596
height: 0.013023327404569737
confidence: 99.5
- text: of
bounding_box:
left: 0.856810948825615
top: 0.2144274182514708
width: 0.0165781289705829
height: 0.013023327404569737
confidence: 99.6
- text: the
bounding_box:
left: 0.856810948825615
top: 0.2287505130660829
width: 0.023959631650915424
height: 0.013023327404569737
confidence: 99.7
- text: character
bounding_box:
left: 0.856810948825615
top: 0.24915344096319605
width: 0.06449739227241374
height: 0.013023327404569737
confidence: 99.4
- text: image
bounding_box:
left: 0.856810948825615
top: 0.29733034614858395
width: 0.04300632873098657
height: 0.013459433575044541
confidence: 99.5
- text: and
bounding_box:
left: 0.856810948825615
top: 0.3320563688603092
width: 0.025181814881593486
height: 0.013459433575044541
confidence: 99.6
bounding_box:
left: 0.856193806798243
top: 0.07725920098508687
width: 0.38698435363448247
height: 0.013023327404569737
confidence: null
- text: the edge of the character. The profiles describe well the
words:
- text: the
bounding_box:
left: 0.8764626871089922
top: 0.07769530715556164
width: 0.0215031643654933
height: 0.01302332740456966
confidence: 99.6
- text: edge
bounding_box:
left: 0.8764626871089922
top: 0.0954901491312081
width: 0.03318045958929803
height: 0.01302332740456966
confidence: 99.3
- text: of
bounding_box:
left: 0.8764626871089922
top: 0.12110069776987276
width: 0.015973087767276968
height: 0.01302332740456966
confidence: 99.3
- text: the
bounding_box:
left: 0.8764626871089922
top: 0.13499623751539197
width: 0.023342489623543374
height: 0.01302332740456966
confidence: 99.6
- text: character.
bounding_box:
left: 0.8764626871089922
top: 0.15409084690108085
width: 0.06879318481588594
height: 0.013459433575044465
confidence: 99.0
- text: The
bounding_box:
left: 0.8764626871089922
top: 0.20531194417841017
width: 0.025181814881593486
height: 0.013459433575044465
confidence: 99.6
- text: profiles
bounding_box:
left: 0.8764626871089922
top: 0.22571487207552335
width: 0.05343723907598105
height: 0.013459433575044465
confidence: 99.3
- text: describe
bounding_box:
left: 0.8764626871089922
top: 0.266076070597893
width: 0.05836227447089146
height: 0.01302332740456966
confidence: 99.3
- text: well
bounding_box:
left: 0.8764626871089922
top: 0.3099175673826789
width: 0.03010685027650385
height: 0.01302332740456966
confidence: 99.3
- text: the
bounding_box:
left: 0.8764626871089922
top: 0.3337922424408264
width: 0.022120306392865285
height: 0.012587221234094932
confidence: 99.5
bounding_box:
left: 0.8764626871089922
top: 0.07769530715556164
width: 0.3845278863490604
height: 0.013450882473662551
confidence: null
- text: external shapes of characters and allow distinguishing
words:
- text: external
bounding_box:
left: 0.8973487094471134
top: 0.07683164591599398
width: 0.05835017364682536
height: 0.013023327404569737
confidence: 99.1
- text: shapes
bounding_box:
left: 0.8967315674197414
top: 0.12067314270077986
width: 0.04912934570844274
height: 0.013450882473662627
confidence: 99.4
- text: of
bounding_box:
left: 0.8961265262164354
top: 0.15799870023258994
width: 0.017812413025327024
height: 0.013450882473662627
confidence: 99.6
- text: characters
bounding_box:
left: 0.8961265262164354
top: 0.17319400738815158
width: 0.0724718353319861
height: 0.013450882473662627
confidence: 99.4
- text: and
bounding_box:
left: 0.8961265262164354
top: 0.22701463948556572
width: 0.026416098936337586
height: 0.013450882473662627
confidence: 99.5
- text: allow
bounding_box:
left: 0.8961265262164354
top: 0.25001710220276374
width: 0.03562482605065405
height: 0.013450882473662627
confidence: 99.4
- text: distinguishing
bounding_box:
left: 0.8961265262164354
top: 0.2812713777534546
width: 0.09766575103764569
height: 0.013023327404569737
confidence: 99.1
bounding_box:
left: 0.8955093841890633
top: 0.07595943357504446
width: 0.38882367889253255
height: 0.013450882473662551
confidence: null
- text: between a great number of letters, such as "p" and "q".
words:
- text: between
bounding_box:
left: 0.9170004477304905
top: 0.07769530715556164
width: 0.05651084838877528
height: 0.012151115063620129
confidence: 99.3
- text: a
bounding_box:
left: 0.9163954065271845
top: 0.1202370365303051
width: 0.008591585086944442
height: 0.012587221234094932
confidence: 99.5
- text: great
bounding_box:
left: 0.9163954065271845
top: 0.12891640443289096
width: 0.03255121673785984
height: 0.012587221234094932
confidence: 99.5
- text: number
bounding_box:
left: 0.9163954065271845
top: 0.15452695307155562
width: 0.05343723907598107
height: 0.013023327404569737
confidence: 99.5
- text: of
bounding_box:
left: 0.9157782644998125
top: 0.19489670269530715
width: 0.013504519657788714
height: 0.013450882473662627
confidence: 99.5
- text: letters,
bounding_box:
left: 0.9157782644998125
top: 0.20704781775892736
width: 0.04730212127445877
height: 0.013450882473662627
confidence: 99.2
- text: such
bounding_box:
left: 0.9157782644998125
top: 0.24307360788069504
width: 0.03132903350718186
height: 0.013450882473662627
confidence: 99.3
- text: as
bounding_box:
left: 0.9157782644998125
top: 0.26782049527979207
width: 0.01350451965778866
height: 0.013450882473662627
confidence: 99.5
- text: '"p"'
bounding_box:
left: 0.9157782644998125
top: 0.2799716103434122
width: 0.02579895690896551
height: 0.013450882473662627
confidence: 77.0
- text: and
bounding_box:
left: 0.9157782644998125
top: 0.30080209330961827
width: 0.023959631650915424
height: 0.013450882473662627
confidence: 99.6
- text: '"q".'
bounding_box:
left: 0.9157782644998125
top: 0.3203413599671638
width: 0.029477607425065628
height: 0.013886988644137354
confidence: 76.7
bounding_box:
left: 0.9151611224724404
top: 0.07725920098508687
width: 0.37346773315262766
height: 0.013886988644137354
confidence: null
- text: a
words:
- text: a
bounding_box:
left: 0.9765849054320599
top: 0.18404535504172936
width: 0.049141446532508855
height: 0.06120023258995759
confidence: 95.7
bounding_box:
left: 0.9790413727174822
top: 0.17405766862771924
width: 0.09520928375222353
height: 0.059028252838965706
confidence: null
- text: 'Figure 6: Profiling'
words:
- text: Figure
bounding_box:
left: 1.1252193274361986
top: 0.16711417430565056
width: 0.047907162477764755
height: 0.013450882473662551
confidence: 99.5
- text: '6:'
bounding_box:
left: 1.1258364694635705
top: 0.20401217676836778
width: 0.014121661685160736
height: 0.013886988644137354
confidence: 99.5
- text: Profiling
bounding_box:
left: 1.1258364694635705
top: 0.21659939800246272
width: 0.06264596619029757
height: 0.013886988644137354
confidence: 98.2
bounding_box:
left: 1.1246021854088264
top: 0.16667806813517583
width: 0.13268553588499377
height: 0.013459433575044389
confidence: null
- text: '4. Structural features:'
words:
- text: '4.'
bounding_box:
left: 1.165139946030325
top: 0.05599261184840608
width: 0.014133762509226867
height: 0.013023327404569737
confidence: 99.3
- text: Structural
bounding_box:
left: 1.165139946030325
top: 0.06857983308250103
width: 0.07432326141410228
height: 0.012587221234095084
confidence: 98.1
- text: 'features:'
bounding_box:
left: 1.1657570880576966
top: 0.12370878369133946
width: 0.06571957550309175
height: 0.011715008893145477
confidence: 98.7
bounding_box:
left: 1.165139946030325
top: 0.05555650567793132
width: 0.16278028533743147
height: 0.012587221234094932
confidence: null
- text: Structural features are based on topological and geometrical
words:
- text: Structural
bounding_box:
left: 1.205072665448517
top: 0.05599261184840608
width: 0.06757100158520796
height: 0.012151115063620129
confidence: 98.2
- text: features
bounding_box:
left: 1.205072665448517
top: 0.10634149678478588
width: 0.05528866515809725
height: 0.012578670132713094
confidence: 98.2
- text: are
bounding_box:
left: 1.205072665448517
top: 0.14801101381857984
width: 0.023342489623543346
height: 0.013014776303187746
confidence: 99.3
- text: based
bounding_box:
left: 1.205072665448517
top: 0.16667806813517583
width: 0.03993271941819236
height: 0.013014776303187746
confidence: 99.4
- text: 'on'
bounding_box:
left: 1.205072665448517
top: 0.1983684498563415
width: 0.017195270997954974
height: 0.013014776303187746
confidence: 99.6
- text: topological
bounding_box:
left: 1.205072665448517
top: 0.21355520591052127
width: 0.07863115478164068
height: 0.013450882473662551
confidence: 99.2
- text: and
bounding_box:
left: 1.205072665448517
top: 0.27171979750991926
width: 0.025193915705659577
height: 0.013014776303187746
confidence: 99.5
- text: geometrical
bounding_box:
left: 1.205072665448517
top: 0.29299493774798196
width: 0.07985333801231859
height: 0.013014776303187746
confidence: 98.6
bounding_box:
left: 1.2044555234211451
top: 0.05512895060883842
width: 0.41706700226285415
height: 0.013014776303187899
confidence: null
- text: properties of the character, such as aspect ratio, cross points,
words:
- text: properties
bounding_box:
left: 1.2259465869625723
top: 0.05555650567793132
width: 0.06941032684325803
height: 0.013459433575044541
confidence: 99.3
- text: of
bounding_box:
left: 1.2253294449352001
top: 0.1072137091257354
width: 0.014738803712532814
height: 0.013023327404569737
confidence: 99.5
- text: the
bounding_box:
left: 1.2253294449352001
top: 0.1202370365303051
width: 0.022108205568799246
height: 0.013023327404569737
confidence: 99.7
- text: character,
bounding_box:
left: 1.2253294449352001
top: 0.13846798467642632
width: 0.0669538595578359
height: 0.013450882473662551
confidence: 99.2
- text: such
bounding_box:
left: 1.2247244037318943
top: 0.18838076344233137
width: 0.031946175534553936
height: 0.013450882473662551
confidence: 99.3
- text: as
bounding_box:
left: 1.2247244037318943
top: 0.2144274182514708
width: 0.014738803712532814
height: 0.013450882473662551
confidence: 99.5
- text: aspect
bounding_box:
left: 1.2247244037318943
top: 0.22745074565604048
width: 0.042994227906920486
height: 0.013450882473662551
confidence: 99.5
- text: ratio,
bounding_box:
left: 1.2247244037318943
top: 0.26044089478724863
width: 0.036229867253959984
height: 0.013023327404569737
confidence: 99.3
- text: cross
bounding_box:
left: 1.2253294449352001
top: 0.28865097824599806
width: 0.03562482605065405
height: 0.013023327404569737
confidence: 99.5
- text: points,
bounding_box:
left: 1.2253294449352001
top: 0.31643350663565467
width: 0.04668497924708669
height: 0.012587221234094932
confidence: 96.7
bounding_box:
left: 1.2247244037318943
top: 0.05512895060883842
width: 0.4164619610595481
height: 0.013450882473662551
confidence: null
- text: loops, branch points, strokes and their directions, inflection
words:
- text: loops,
bounding_box:
left: 1.2449932840426434
top: 0.05555650567793132
width: 0.0442285119616646
height: 0.013886988644137354
confidence: 99.4
- text: branch
bounding_box:
left: 1.2443761420152712
top: 0.08941886715008894
width: 0.04668497924708672
height: 0.013886988644137354
confidence: 99.5
- text: points,
bounding_box:
left: 1.2443761420152712
top: 0.12588076344233137
width: 0.048524304505136805
height: 0.013450882473662551
confidence: 99.3
- text: strokes
bounding_box:
left: 1.2443761420152712
top: 0.1627702148036667
width: 0.05037573058725298
height: 0.013450882473662551
confidence: 99.3
- text: and
bounding_box:
left: 1.2443761420152712
top: 0.20096798467642632
width: 0.02579895690896551
height: 0.013450882473662551
confidence: 99.6
- text: their
bounding_box:
left: 1.2443761420152712
top: 0.22180701874401423
width: 0.0343905419959099
height: 0.013450882473662551
confidence: 99.5
- text: directions,
bounding_box:
left: 1.2443761420152712
top: 0.2487173347927213
width: 0.07370611938673026
height: 0.013023327404569737
confidence: 97.2
- text: inflection
bounding_box:
left: 1.2443761420152712
top: 0.303410179231085
width: 0.0657195755030918
height: 0.013023327404569737
confidence: 99.1
bounding_box:
left: 1.2443761420152712
top: 0.05555650567793132
width: 0.4164619610595482
height: 0.013886988644137354
confidence: null
- text: between two points, horizontal curves at top or bottom, etc.
words:
- text: between
bounding_box:
left: 1.2652621643533926
top: 0.05642871801888084
width: 0.0558937063614032
height: 0.012587221234094932
confidence: 99.5
- text: two
bounding_box:
left: 1.2646450223260206
top: 0.09853434122314955
width: 0.02579895690896548
height: 0.013023327404569584
confidence: 99.6
- text: points,
bounding_box:
left: 1.2646450223260206
top: 0.11936482418935558
width: 0.045462796016408735
height: 0.013023327404569584
confidence: 99.4
- text: horizontal
bounding_box:
left: 1.2646450223260206
top: 0.15409084690108085
width: 0.07002746887063009
height: 0.013459433575044389
confidence: 99.3
- text: curves
bounding_box:
left: 1.2640278802986484
top: 0.20617560541797783
width: 0.04484565398903666
height: 0.013459433575044389
confidence: 99.5
- text: at
bounding_box:
left: 1.2640278802986484
top: 0.2404740730606102
width: 0.011665194399738628
height: 0.013023327404569584
confidence: 99.6
- text: top
bounding_box:
left: 1.2646450223260206
top: 0.25132542071418795
width: 0.022725347596171324
height: 0.013023327404569584
confidence: 99.6
- text: or
bounding_box:
left: 1.2646450223260206
top: 0.2699839239294021
width: 0.014750904536598905
height: 0.013023327404569584
confidence: 99.7
- text: bottom,
bounding_box:
left: 1.2646450223260206
top: 0.2830072513339718
width: 0.05283219787267512
height: 0.013023327404569584
confidence: 99.3
- text: etc.
bounding_box:
left: 1.2646450223260206
top: 0.32294089478724863
width: 0.024576773678287447
height: 0.013023327404569584
confidence: 99.0
bounding_box:
left: 1.2640278802986484
top: 0.05599261184840608
width: 0.4029453405776934
height: 0.013023327404569737
confidence: null
- text: 'Global Transformations-Moments:'
words:
- text: Global
bounding_box:
left: 0.1259090744079672
top: 0.36548262416199206
width: 0.04914144653250891
height: 0.012587221234094951
confidence: 99.4
- text: 'Transformations-Moments:'
bounding_box:
left: 0.1259090744079672
top: 0.4023720755233274
width: 0.18428344970292476
height: 0.012151115063620186
confidence: 96.6
bounding_box:
left: 0.12529193238059513
top: 0.36548262416199206
width: 0.23709144592746764
height: 0.012587221234094951
confidence: null
- text: The Fourier Transform (FT) of the contour of the image is
words:
- text: The
bounding_box:
left: 0.1652246517987875
top: 0.3663462854015597
width: 0.025798956908965456
height: 0.012587221234094951
confidence: 99.7
- text: Fourier
bounding_box:
left: 0.1652246517987875
top: 0.3880489807087153
width: 0.05283219787267506
height: 0.012587221234094951
confidence: 99.4
- text: Transform
bounding_box:
left: 0.1652246517987875
top: 0.4297184977425092
width: 0.0644973922724138
height: 0.012587221234094951
confidence: 99.3
- text: (FT)
bounding_box:
left: 0.1652246517987875
top: 0.4826754686003557
width: 0.03378550079260402
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- text: parameter specific. OCR has been implemented in various
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- text: countries for recognizing different languages as well.
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- text: References
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- text: '[1] Oivind Due Trier, Anil K. Jain, Torfinn Taxt, "Feature'
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- text: Extraction Methods for Character Recognition-A
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- text: Survey", July 1995
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- text: '[2] Yasser Alginahi, Taibah University Kingdom of Saudi'
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- text: Techniques
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- text: Recognition"
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- text: '[3] Om Prakash Sharma, M. K. Ghose, Krishna Bikram'
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- text: Shah, Benoy Kumar Thakur, "Recent Trends and Tools
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raw_text: "# An Introduction to the Process of Optical Character Recognition \n\nUmal Patel ${ }^{1}$
${ }^{1}$ Department of Computer Engineering
L D College Of Engineering, Gujarat Technological University, Gujarat, India\n\n\n#### Abstract\n\nThis paper presents an overview of methods and techniques used for feature extraction that helps in efficient classification of the alphabets and numbers of English language. Character recognition has long been a essential area for research since years. Recognition of character is a minor work for humans, but to make a computer program that does character recognition is extremely difficult. Hence to make a machine recognize the characters and efficiently determine a pattern has been the primary concern for researchers now days This paper discusses various offline and online Optical Character Recognition Techniques (OCR).\n\n\nKeywords: OCR, online, offline, online, zoning, euler number.\n\n## 1. Introduction\n\nOCR is an approach that provides a full alphanumeric recognition of printed or handwritten characters at electronically by simply scanning them and generating into a form that can be scanned through a scanner and then the recognition engine of the OCR system interpret the images and turn images of handwritten or printed characters into ASCII data (machine-readable characters).Character recognition also popularly referred as optical character recognition (OCR) is a field of research that has immense potential in future where we want to track and locate every piece of information being exchanged. The problem with the hand written text is due to uncertainties such as variation in calligraphy over period of time, similarity in text, variation in styles of writing [3] The character recognition system helps in making the communication between a human and a computer easy.[4] The character recognition is basically classified into two types: offline handwritten text recognition, online handwritten text recognition. Offline means the text written on the plain paper or sheet and then the writing is usually captured optically by a scanner and the completed writing is available as an image. Online means the text written on any digital devices such as tablets using stylus i.e. the two dimensional coordinates of successive points are represented as a function of time and the order of strokes made by the writer are also available.[6]\n\n## 2. Applications of optical character recognition\n\nThe area of OCR is becoming an integral part of document scanners, and is used in many applications such as postal processing, script recognition, banking, security (i.e. passport authentication) and language identification, document reading, mail sorting, signature verification, writer identification., license plate recognition system, smart card processing system, automatic data entry, bank cheque /DD processing, money counting machine, postal automation, address and zip code recognition etc many organizations are depending on OCR systems to eliminate the human interactions for better performance and efficiency $[2,4,6,7]$.\n\n## 3. Potential problem areas for OCR\n\n1. The same characters differ in sizes, shapes and styles from person to person and even from time to time with the same person. The source of confusion is the high level of abstraction: there are thousands styles of type in common use plus variations in calligraphy and a character recognition program must recognize most of these.\n2. Like any image, visual characters are subject to spoilage due to noise. Some images containing characters are already blurred or not clear which makes them difficult to process. Noise consists of random changes to a pattern, particularly near the edges. A character with much noise may be interpreted as a completely different character by a computer program.\n3. There are no hard-and-fast rules that define the appearance of a visual character. Hence rules need to be heuristically deduced from the samples.\n\n## 4. Phases of OCR\n\n| Data Acquisition |\n| :--: |\n| Pre processing |\n| Segmentation |\n| Normalization |\n| Feature Extraction |\n| Classification |\n| Post Processing |## 1. Data Acquisition\n\nMost Important initial phase in OCR is to gather the image from either device sensor like PDA or tablets in case on online recognition or getting the images containing characters directly for offline recognition.\n\nIn Image acquisition, the recognition system acquires a scanned image as an input image. The image should have a specific format such as JPEG, BMP etc. This image is acquired through a scanner, digital camera or any other suitable digital input device. Data samples for the experiment have been collected from different individuals [9].\n\n## 2. Pre Processing\n\nThe goal of pre-processing is to simplify the pattern recognition problem without missing any vital information. It reduces the noises and inconsistent data. It enhances the image and prepares it for the next steps [3].\n\nPreprocessing is the preliminary step which transforms the data into a format that will be more easily and effectively processed. Therefore, the main task in preprocessing the captured data is to decrease the variation that causes a reduction in the recognition rate and increases the complexities, as for example, preprocessing of the input raw stroke of characters is crucial for the success of efficient character recognition systems. Thus, preprocessing is an essential stage prior to feature extraction since it controls the suitability of the results for the successive stages [2].\n\nPreprocessing can be done through various ways Binarization, Noise reduction, Stroke width normalization, Skew correction, Slant removal, Filtering, Morphological Operations, Noise Modelling, Skew Normalization, Size Normalization, Contour Smoothing, Compression, Thresholding, Thinning etc\n\n\nFigure 1: Slant Removal\n\n\nFigure 2: Normalization of ' e ' and ' 1 ' as in [9]\n\n## 3. Segmentation\n\nSegmentation is an integral part of any text based recognition system. It assures efficiency of classification and\nrecognition. Accuracy of character recognition heavily depends upon segmentation phase.\n\n\nFigure 3: Segmentation [9]\n\n## 4. Normalization\n\nThe results of segmentation process provides isolated characters which are ready to pass through feature extraction stage, thus the isolated characters are reduced to a specific size depending on the methods used. The segmentation process essentially renders the image in the form of $\\mathrm{m} * \\mathrm{n}$ matrix. These matrices are then generally normalized by reducing the size and removing the redundant information from the image without losing any important information.\n\n## 5. Feature Extraction\n\nFeature extraction is the process of extracting the relevant features from objects/alphabets to form a feature vectors. These feature vectors is then used by classifiers to recognize the input unit with target output unit. It becomes easier for the classifier to classify between different classes by looking at these features as it allows fairly easy to distinguish.\n\nFeature extraction is also defined as extracting the raw data the information which is most relevant for classification purposes in the sense of minimizing the pattern variability.[1]\nDue to the nature of handwriting with its high degree of variability and imprecision obtaining these features, is a difficult task. Feature extraction methods are based on 3 types of features:\n\n- Statistical\n- Structural\n- Global transformations and moments\n\nStatistical Features includes:\n\n## 1. Zoning\n\nThe character image is divided into NxM zones. From each zone features are extracted to form the feature vector. Thegoal of zoning is to obtain the local characteristics instead of global characteristics.\n\n\nFigure 4: zoning\nAfter dividing the character into different zones you can compare the density or direction features of it and classify each one of them.\n\n## 2. Projection Histograms\n\nThe basic idea behind using projections is that character images, which are 2-D signals, can be represented as 1D signal. These features, although independent to noise and deformation, depend on rotation. Projection histograms count the number of pixels in each column and row of a character image. Projection histograms can separate characters such as \"m\" and \"n\".\n\n\nFigure 5: Projection Histogram\n\n## 3. Profiles\n\nThe profile counts the number of pixels (distance) between the bounding box of the character image and the edge of the character. The profiles describe well the external shapes of characters and allow distinguishing between a great number of letters, such as \"p\" and \"q\".\n\n\nFigure 6: Profiling\n\n## 4. Structural features:\n\nStructural features are based on topological and geometrical properties of the character, such as aspect ratio, cross points, loops, branch points, strokes and their directions, inflection between two points, horizontal curves at top or bottom, etc.\n\nGlobal Transformations-Moments:\nThe Fourier Transform (FT) of the contour of the image is calculated. Since the first $n$ coefficients of the FT can be used in order to reconstruct the contour, then these $n$ coefficients are considered to be a $n$-dimensional feature vector that represents the character.\n\n\nFigure 7: Contouring\n\n## 6 Classification\n\nThe results Classification is the last stage where we train the neural net using the feature vectors obtained during feature extraction method against the required targets. To optimize the whole recognition process, several combination methods of multilayer perceptron have been devised. E.g.: k-Nearest Neighbour (k-NN), Bayes Classifier, Neural Networks (NN), Hidden Markov Models (HMM), Support Vector Machines (SVM), etc there is no such thing as the \"best classifier\". The use of classifier depends on many factors, such as available training set, number of free parameters etc.\n\n## 7. Post Processing\n\nThe goal of post processing is the incorporation of context and shape information in all the stages of OCR systems is necessary for meaningful improvements in recognition rates.\n\n## 5. Conclusion\n\nThe character recognition methods have been introduced and developed over the years. In this paper, I have tried to explain the overview of the whole OCR process and the methods related to it. Many researchers try to hybrid two or more different methods and compare the results for efficiency but again this will be application specific and parameter specific. OCR has been implemented in various countries for recognizing different languages as well.\n\n## References\n\n[1] Oivind Due Trier, Anil K. Jain, Torfinn Taxt, \"Feature Extraction Methods for Character Recognition-A Survey\", July 1995\n[2] Yasser Alginahi, Taibah University Kingdom of Saudi Arabia, \"Preprocessing Techniques in Character Recognition\"\n[3] Om Prakash Sharma, M. K. Ghose, Krishna Bikram Shah, Benoy Kumar Thakur, \"Recent Trends and Tools for Feature Extraction\n[4] in OCR Technology\", International Journal of Soft Computing and Engineering (IJSCE)\n[5] ISSN: 2231-2307, Volume-2, Issue-6, January 2013[6] Suruchi G. Dedgaonkar, Anjali A. Chandavale, Ashok M. Sapkal, \"Survey of Methods for Character Recognition\", International Journal of Engineering and Innovative Technology (IJEIT) Volume 1, Issue 5, May 2012\n[7] Mohanad Alata, Mohammad Al-Shabi \"TEXT DETECTION AND CHARACTER\n[8] RECOGNITION USING FUZZY IMAGE PROCESSING\", Journal of ELECTRICAL ENGINEERING, VOL. 57, NO. 5, 2006, 258-267\n[9] Rejean Plamondon, Fellow, IEEE and Sargur N. Shrihari, Fellow, IEEE, \"On-line and Off-line Handwriting Recognition: A comprehensive Survey\", IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL 22, NO. 1 JANUARY 2000\n[10]Om Prakash Sharma, M. K. Ghose, Krishna Bikram Shah, \"An Improved Zone Based Hybrid Feature Extraction Model for Handwritten Alphabets Recognition Using Euler Number\", International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-2, Issue-2, May 2012\n[11]J. Pradeepa,, E. Srinivasan, S. Himavathi, \"Neural Network Based Recognition System Integrating Feature\n\nExtraction and Classification for English Handwritten\", IJE TRANSACTIONS B: Applications Vol. 25, No. 2, (May 2012) 99-106\n[12] Nafiz Arica and Fatos T. Yarman-Vural, \"An Overview of Character Recognition Focused on\n[13]Off-Line Handwriting\", IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART C: APPLICATIONS AND REVIEWS, VOL. 31, NO. 2, MAY 2001\n\n## Author Profile\n\nUmal Patel, pursuing her Master Degree in Computer Science \\& Technology from Gujarat Technological University (L D College of Eng., Ahmedabad), received her Bachelor Degree in Computer Engg. from Gujarat University in 2008. Presently working as Assistant Professor in Department of MCA, L.J Institute of Technology. Earlier he has served as Software Test Engineer in Lodestone Software Services since June 2008. Her area of interest is Compilers and Image Processing."
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raw_text: 'International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064
An Introduction to the Process of Optical Character
Recognition
Umal Patel1
1
Department of Computer Engineering
LD College Of Engineering, Gujarat Technological University, Gujarat, India
Abstract: This paper presents an overview of methods and techniques used for feature extraction that helps in efficient classification
of the alphabets and numbers of English language. Character recognition has long been a essential area for research since years.
Recognition of character is a minor work for humans, but to make a computer program that does character recognition is extremely
difficult. Hence to make a machine recognize the characters and efficiently determine a pattern has been the primary concern for
researchers now days This paper discusses various offline and online Optical Character Recognition Techniques (OCR).
Keywords: OCR, online, offline, online, zoning, euler number.
1. Introduction
3. Potential problem areas for OCR
OCR is an approach that provides a full alphanumeric
1. The same characters differ in sizes, shapes and styles
recognition of printed or handwritten characters at
from person to person and even from time to time with
electronically by simply scanning them and generating into a
the same person. The source of confusion is the high
form that can be scanned through a scanner and then the
level of abstraction: there are thousands styles of type in
recognition engine of the OCR system interpret the images
common use plus variations in calligraphy and a
and turn images of handwritten or printed characters into
character recognition program must recognize most of
ASCII data (machine-readable characters). Character
these.
recognition also popularly referred as optical character
2. Like any image, visual characters are subject to spoilage
recognition (OCR) is a field of research that has immense
due to noise. Some images containing characters are
potential in future where we want to track and locate every
already blurred or not clear which makes them difficult to
piece of information being exchanged. The problem with the
process. Noise consists of random changes to a pattern,
hand written text is due to uncertainties such as variation in
particularly near the edges. A character with much noise
calligraphy over period of time, similarity in text, variation
may be interpreted as a completely different character by
in styles of writing [3] The character recognition system
a computer program.
helps in making the communication between a human and a
3. There are no hard-and-fast rules that define the
computer easy.[4] The character recognition is basically
appearance of a visual character. Hence rules need to be
classified into two types: offline handwritten text
heuristically deduced from the samples.
recognition, online handwritten text recognition. Offline
means the text written on the plain paper or sheet and then
4. Phases of OCR
the writing is usually captured optically by a scanner and the
completed writing is available as an image. Online means the
Data Acquisition
text written on any digital devices such as tablets using
stylus i.e. the two dimensional coordinates of successive
points are represented as a function of time and the order of
Pre processing
strokes made by the writer are also available.[6]
Segmentation
2. Applications
of
optical
character
recognition
Normalization
The area of OCR is becoming an integral part of document
Feature Extraction
scanners, and is used in many applications such as postal
processing, script recognition, banking, security (i.e.
Classification
passport authentication) and language identification,
document reading, mail sorting, signature verification, writer
identification., license plate recognition system, smart card
Post Processing
processing system, automatic data entry, bank cheque /DD
processing, money counting machine, postal automation,
address and zip code recognition etc many organizations are
depending on OCR systems to eliminate the human
interactions for better performance and efficiency [2,4,6,7].
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International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064
1. Data Acquisition
recognition. Accuracy of character recognition heavily
depends upon segmentation phase.
Most Important initial phase in OCR is to gather the image
from either device sensor like PDA or tablets in case on
online recognition or getting the images containing
characters directly for offline recognition.
radyape
In Image acquisition, the recognition system acquires a
scanned image as an input image. The image should have a
specific format such as JPEG, BMP etc. This image is
acquired through a scanner, digital camera or any other
suitable digital input device. Data samples for the
experiment have been collected from different individuals
[9].
2. Pre Processing
eighteen
eighteen
The goal of pre-processing is to simplify the pattern
recognition problem without missing any vital information.
It reduces the noises and inconsistent data. It enhances the
image and prepares it for the next steps [3].
ei gliteen
Figure 3: Segmentation [9]
Preprocessing is the preliminary step which transforms the
data into a format that will be more easily and effectively
4. Normalization
processed. Therefore, the main task in preprocessing the
captured data is to decrease the variation that causes a
reduction in the recognition rate and increases the
The results of segmentation process provides isolated
characters which are ready to pass through feature extraction
complexities, as for example, preprocessing of the input raw
stroke of characters is crucial for the success of efficient
stage, thus the isolated characters are reduced to a specific
size depending on the methods used. The segmentation
character recognition systems. Thus, preprocessing is an
process essentially renders the image in the form of m*n
essential stage prior to feature extraction since it controls the
suitability of the results for the successive stages [2].
matrix. These matrices are then generally normalized by
reducing the size and removing the redundant information
from the image without losing any important information.
Preprocessing can be done through various ways
Binarization, Noise reduction, Stroke width normalization,
5. Feature Extraction
Skew correction, Slant removal, Filtering, Morphological
Operations, Noise Modelling, Skew Normalization, Size
Feature extraction is the process of extracting the relevant
Normalization, Contour Smoothing, Compression,
features from objects/alphabets to form a feature vectors.
Thresholding, Thinning etc
These feature vectors is then used by classifiers to recognize
start
the input unit with target output unit. It becomes easier for
the classifier to classify between different classes by looking
at these features as it allows fairly easy to distinguish.
start
Feature extraction is also defined as extracting the raw data
the information which is most relevant for classification
Figure 1: Slant Removal
purposes in the sense of minimizing the pattern
variability.[1]
Due to the nature of handwriting with its high degree of
variability and imprecision obtaining these features, is a
difficult task. Feature extraction methods are based on 3
types of features:
Statistical
Structural
Global transformations and moments
Figure 2: Normalization of ''e'' and ''l'' as in [9]
Statistical Features includes:
3. Segmentation
1. Zoning
Segmentation is an integral part of any text based
recognition system. It assures efficiency of classification and
The character image is divided into NxM zones. From each
zone features are extracted to form the feature vector. The
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156
International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064
goal of zoning is to obtain the local characteristics instead of
global characteristics.
Global Transformations-Moments:
The Fourier Transform (FT) of the contour of the image is
calculated. Since the first n coefficients of the FT can be
used in order to reconstruct the contour, then these n
coefficients are considered to be a n-dimensional feature
vector that represents the character.
Figure 4: zoning
OOSSESSS
After dividing the character into different zones you can
compare the density or direction features of it and
classify each one of them.
55555555
2. Projection Histograms
Figure 7: Contouring
The basic idea behind using projections is that character
6 Classification
images, which are 2-D signals, can be represented as 1-
D signal. These features, although independent to noise
The results Classification is the last stage where we train the
and deformation, depend on rotation. Projection
neural net using the feature vectors obtained during feature
histograms count the number of pixels in each column
extraction method against the required targets. To optimize
and row of a character image. Projection histograms can
the whole recognition process, several combination methods
separate characters such as "m" and "n".
of multilayer perceptron have been devised. E.g.: k-Nearest
Neighbour (k-NN), Bayes Classifier, Neural Networks (NN),
Hidden Markov Models (HMM), Support Vector Machines
(SVM), etc there is no such thing as the "best classifier". The
use of classifier depends on many factors, such as available
training set, number of free parameters etc.
7. Post Processing
The goal of post processing is the incorporation of context
and shape information in all the stages of OCR systems is
Figure 5: Projection Histogram
necessary for meaningful improvements in recognition rates.
3. Profiles
5. Conclusion
The profile counts the number of pixels (distance)
between the bounding box of the character image and
The character recognition methods have been introduced and
the edge of the character. The profiles describe well the
developed over the years. In this paper, I have tried to
external shapes of characters and allow distinguishing
explain the overview of the whole OCR process and the
between a great number of letters, such as "p" and "q".
methods related to it. Many researchers try to hybrid two or
more different methods and compare the results for
efficiency but again this will be application specific and
parameter specific. OCR has been implemented in various
countries for recognizing different languages as well.
References
[1] Oivind Due Trier, Anil K. Jain, Torfinn Taxt, "Feature
Extraction Methods for Character Recognition-A
Survey", July 1995
Figure 6: Profiling
[2] Yasser Alginahi, Taibah University Kingdom of Saudi
Arabia, "Preprocessing Techniques in Character
4. Structural features:
Recognition"
[3] Om Prakash Sharma, M. K. Ghose, Krishna Bikram
Structural features are based on topological and geometrical
Shah, Benoy Kumar Thakur, "Recent Trends and Tools
properties of the character, such as aspect ratio, cross points,
for Feature Extraction
loops, branch points, strokes and their directions, inflection
[4] in OCR Technology", International Journal of Soft
between two points, horizontal curves at top or bottom, etc.
Computing and Engineering (IJSCE)
[5] ISSN: 2231-2307, Volume-2, Issue-6, January 2013
Volume 2 Issue 5, May 2013
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157
International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064
[6] Suruchi G. Dedgaonkar, Anjali A. Chandavale, Ashok
Extraction and Classification for English Handwritten",
M. Sapkal, "Survey of Methods for Character
IJE TRANSACTIONS B: Applications Vol. 25, No. 2,
Recognition", International Journal of Engineering and
(May 2012) 99-106
Innovative Technology (IJEIT) Volume 1, Issue 5, May
[12] Nafiz Arica and Fatos T. Yarman-Vural, "An Overview
2012
of Character Recognition Focused on
[7] Mohanad Alata, Mohammad Al-Shabi "TEXT [13] Off-Line Handwriting", IEEE TRANSACTIONS ON
DETECTION AND CHARACTER
SYSTEMS, MAN, AND CYBERNETICS-PART C:
[8] RECOGNITION USING FUZZY IMAGE
APPLICATIONS AND REVIEWS, VOL. 31, NO. 2,
PROCESSING",
Journal
of
ELECTRICAL
MAY 2001
ENGINEERING, VOL. 57, NO. 5, 2006, 258-267
[9] Rejean Plamondon, Fellow, IEEE and Sargur N.
Author Profile
Shrihari, Fellow, IEEE, "On-line and Off-line
Handwriting Recognition: A comprehensive Survey",
Umal Patel, pursuing her Master Degree in Computer
IEEE TRANSACTIONS ON PATTERN ANALYSIS
Science & Technology from Gujarat Technological
AND MACHINE INTELLIGENCE, VOL 22, NO. 1
University (L D College of Eng., Ahmedabad), received her
JANUARY 2000
Bachelor Degree in Computer Engg. from Gujarat
[10] Om Prakash Sharma, M. K. Ghose, Krishna Bikram
University in 2008. Presently working as Assistant Professor
Shah, "An Improved Zone Based Hybrid Feature
in Department of MCA, L.J Institute of Technology. Earlier
Extraction Model for Handwritten Alphabets
he has served as Software Test Engineer in Lodestone
Recognition Using Euler Number", International Journal
Software Services since June 2008. Her area of interest is
of Soft Computing and Engineering (IJSCE) ISSN:
Compilers and Image Processing.
2231-2307, Volume-2, Issue-2, May 2012
[11]J. Pradeepa,, E. Srinivasan, S. Himavathi, "Neural
Network Based Recognition System Integrating Feature
Volume 2 Issue 5, May 2013
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left: 0.8939876556396484
top: 0.33791297674179077
width: 0.03812398761510849
height: 0.011312923394143581
confidence: 99.97
bounding_box:
left: 0.5197277665138245
top: 0.3373088836669922
width: 0.41238388419151306
height: 0.01244040671736002
confidence: 99.86
- text: recognition of printed or handwritten characters at
words:
- text: recognition
bounding_box:
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width: 0.07667256891727448
height: 0.011122503317892551
confidence: 99.91
- text: of
bounding_box:
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top: 0.3518519103527069
width: 0.015167302452027798
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confidence: 99.99
- text: printed
bounding_box:
left: 0.2045634239912033
top: 0.3518269956111908
width: 0.04792202264070511
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confidence: 99.98
- text: or
bounding_box:
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confidence: 99.96
- text: handwritten
bounding_box:
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confidence: 99.81
- text: characters
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confidence: 99.86
- text: at
bounding_box:
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width: 0.011938633397221565
height: 0.007280179299414158
confidence: 99.96
bounding_box:
left: 0.08034266531467438
top: 0.35142573714256287
width: 0.41399669647216797
height: 0.012010164558887482
confidence: 99.92
- text: from person to person and even from time to time with
words:
- text: from
bounding_box:
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width: 0.03277905657887459
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confidence: 99.99
- text: person
bounding_box:
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width: 0.044992126524448395
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confidence: 99.98
- text: to
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confidence: 99.99
- text: person
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confidence: 99.98
- text: and
bounding_box:
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width: 0.023974068462848663
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confidence: 99.99
- text: even
bounding_box:
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confidence: 99.97
- text: from
bounding_box:
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confidence: 99.99
- text: time
bounding_box:
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confidence: 99.99
- text: to
bounding_box:
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top: 0.35320907831192017
width: 0.01310645043849945
height: 0.007536375895142555
confidence: 99.99
- text: time
bounding_box:
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top: 0.35194796323776245
width: 0.02934904955327511
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confidence: 99.99
- text: with
bounding_box:
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confidence: 99.99
bounding_box:
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top: 0.35138458013534546
width: 0.3895539939403534
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confidence: 99.99
- text: electronically by simply scanning them and generating into a
words:
- text: electronically
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confidence: 99.85
- text: by
bounding_box:
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confidence: 99.99
- text: simply
bounding_box:
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top: 0.36556023359298706
width: 0.04493303969502449
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confidence: 99.97
- text: scanning
bounding_box:
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top: 0.3657899796962738
width: 0.0594044104218483
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confidence: 99.95
- text: them
bounding_box:
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top: 0.3657158315181732
width: 0.03348344936966896
height: 0.008641527965664864
confidence: 99.99
- text: and
bounding_box:
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top: 0.3657742440700531
width: 0.024028893560171127
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confidence: 99.99
- text: generating
bounding_box:
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top: 0.36577340960502625
width: 0.07066712528467178
height: 0.011246386915445328
confidence: 99.97
- text: into
bounding_box:
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width: 0.02547801472246647
height: 0.008462253957986832
confidence: 99.97
- text: a
bounding_box:
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top: 0.3681316673755646
width: 0.0073218257166445255
height: 0.006090113893151283
confidence: 99.89
bounding_box:
left: 0.08040179312229156
top: 0.3651091754436493
width: 0.4139156937599182
height: 0.012330501340329647
confidence: 99.95
- text: the same person. The source of confusion is the high
words:
- text: the
bounding_box:
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confidence: 99.99
- text: same
bounding_box:
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top: 0.36801084876060486
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confidence: 99.96
- text: person.
bounding_box:
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top: 0.36778688430786133
width: 0.04874403774738312
height: 0.008989090099930763
confidence: 99.93
- text: The
bounding_box:
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width: 0.02644268423318863
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confidence: 99.99
- text: source
bounding_box:
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top: 0.3678099811077118
width: 0.04331442341208458
height: 0.006628422532230616
confidence: 99.91
- text: of
bounding_box:
left: 0.7575891017913818
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width: 0.01510973833501339
height: 0.008745885454118252
confidence: 99.99
- text: confusion
bounding_box:
left: 0.7797854542732239
top: 0.365603506565094
width: 0.0662493109703064
height: 0.008914237841963768
confidence: 99.86
- text: is
bounding_box:
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top: 0.3656996190547943
width: 0.010814660228788853
height: 0.008713701739907265
confidence: 99.98
- text: the
bounding_box:
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width: 0.020400267094373703
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confidence: 100.0
- text: high
bounding_box:
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height: 0.011403373442590237
confidence: 99.99
bounding_box:
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width: 0.3898964822292328
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confidence: 99.96
- text: form that can be scanned through a scanner and then the
words:
- text: form
bounding_box:
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width: 0.032930392771959305
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confidence: 99.99
- text: that
bounding_box:
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confidence: 99.99
- text: can
bounding_box:
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width: 0.023505304008722305
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confidence: 99.96
- text: be
bounding_box:
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width: 0.015727359801530838
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confidence: 99.98
- text: scanned
bounding_box:
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width: 0.05423593148589134
height: 0.008949017152190208
confidence: 99.98
- text: through
bounding_box:
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confidence: 99.99
- text: a
bounding_box:
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width: 0.007687370292842388
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confidence: 99.96
- text: scanner
bounding_box:
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top: 0.3819294571876526
width: 0.05224983021616936
height: 0.00650436244904995
confidence: 99.88
- text: and
bounding_box:
left: 0.40520787239074707
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width: 0.02408587373793125
height: 0.00864984467625618
confidence: 99.99
- text: then
bounding_box:
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confidence: 99.99
- text: the
bounding_box:
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confidence: 99.99
bounding_box:
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width: 0.4134919047355652
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confidence: 99.97
- text: 'level of abstraction: there are thousands styles of type in'
words:
- text: level
bounding_box:
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- text: of
bounding_box:
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confidence: 99.98
- text: 'abstraction:'
bounding_box:
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confidence: 99.24
- text: there
bounding_box:
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top: 0.3797324001789093
width: 0.033634308725595474
height: 0.008599288761615753
confidence: 99.99
- text: are
bounding_box:
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confidence: 99.99
- text: thousands
bounding_box:
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top: 0.3796778619289398
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confidence: 99.93
- text: styles
bounding_box:
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width: 0.03768434002995491
height: 0.011020767502486706
confidence: 99.98
- text: of
bounding_box:
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width: 0.01502483431249857
height: 0.008801290765404701
confidence: 99.98
- text: type
bounding_box:
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confidence: 99.96
- text: in
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confidence: 99.96
bounding_box:
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top: 0.37902048230171204
width: 0.38935455679893494
height: 0.012287878431379795
confidence: 99.9
- text: recognition engine of the OCR system interpret the images
words:
- text: recognition
bounding_box:
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confidence: 99.89
- text: engine
bounding_box:
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confidence: 99.79
- text: of
bounding_box:
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top: 0.3933860957622528
width: 0.015025675296783447
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confidence: 99.98
- text: the
bounding_box:
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top: 0.3936137855052948
width: 0.020388754084706306
height: 0.008701235055923462
confidence: 99.99
- text: OCR
bounding_box:
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top: 0.3934060037136078
width: 0.03419875726103783
height: 0.009227477014064789
confidence: 99.94
- text: system
bounding_box:
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top: 0.3950325846672058
width: 0.04658965393900871
height: 0.0096316272392869
confidence: 99.97
- text: interpret
bounding_box:
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top: 0.3934538960456848
width: 0.0569123812019825
height: 0.011279908940196037
confidence: 99.45
- text: the
bounding_box:
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top: 0.3936828076839447
width: 0.020491383969783783
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confidence: 99.99
- text: images
bounding_box:
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top: 0.3934546709060669
width: 0.04704216867685318
height: 0.011169424280524254
confidence: 99.91
bounding_box:
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width: 0.41340309381484985
height: 0.01212066039443016
confidence: 99.88
- text: common use plus variations in calligraphy and a
words:
- text: common
bounding_box:
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width: 0.05834505334496498
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confidence: 99.97
- text: use
bounding_box:
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width: 0.022122154012322426
height: 0.006183503661304712
confidence: 99.97
- text: plus
bounding_box:
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width: 0.02795041725039482
height: 0.011243417859077454
confidence: 99.84
- text: variations
bounding_box:
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top: 0.393528550863266
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confidence: 99.56
- text: in
bounding_box:
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confidence: 99.98
- text: calligraphy
bounding_box:
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- text: and
bounding_box:
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width: 0.023948227986693382
height: 0.009009279310703278
confidence: 99.99
- text: a
bounding_box:
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top: 0.3961326479911804
width: 0.0073463465087115765
height: 0.006212164647877216
confidence: 99.92
bounding_box:
left: 0.5424228310585022
top: 0.39323100447654724
width: 0.39004382491111755
height: 0.01208731159567833
confidence: 99.89
- text: and turn images of handwritten or printed characters into
words:
- text: and
bounding_box:
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top: 0.40757662057876587
width: 0.024244561791419983
height: 0.008795606903731823
confidence: 99.98
- text: turn
bounding_box:
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top: 0.4088791012763977
width: 0.02690941095352173
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confidence: 99.97
- text: images
bounding_box:
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top: 0.4075715243816376
width: 0.04709024354815483
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confidence: 99.93
- text: of
bounding_box:
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width: 0.014941622503101826
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confidence: 99.98
- text: handwritten
bounding_box:
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top: 0.40741217136383057
width: 0.08033958822488785
height: 0.009130134247243404
confidence: 99.73
- text: or
bounding_box:
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top: 0.40988290309906006
width: 0.014235070906579494
height: 0.006677352823317051
confidence: 99.97
- text: printed
bounding_box:
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top: 0.4075440466403961
width: 0.04768478870391846
height: 0.011201661080121994
confidence: 99.98
- text: characters
bounding_box:
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top: 0.40759992599487305
width: 0.06750483065843582
height: 0.00886624213308096
confidence: 99.87
- text: into
bounding_box:
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top: 0.40750229358673096
width: 0.026208139955997467
height: 0.008888397365808487
confidence: 99.96
bounding_box:
left: 0.08050458133220673
top: 0.4069960117340088
width: 0.4135294258594513
height: 0.012108266353607178
confidence: 99.93
- text: character recognition program must recognize most of
words:
- text: character
bounding_box:
left: 0.5422782897949219
top: 0.4075808823108673
width: 0.0622931644320488
height: 0.008923744782805443
confidence: 99.93
- text: recognition
bounding_box:
left: 0.612540066242218
top: 0.4074363708496094
width: 0.07668519765138626
height: 0.011235247366130352
confidence: 99.89
- text: program
bounding_box:
left: 0.6971301436424255
top: 0.41005223989486694
width: 0.05765560641884804
height: 0.008616234175860882
confidence: 99.96
- text: must
bounding_box:
left: 0.7627453804016113
top: 0.40873202681541443
width: 0.0329771563410759
height: 0.007790079340338707
confidence: 99.99
- text: recognize
bounding_box:
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width: 0.06526274979114532
height: 0.011119172908365726
confidence: 99.9
- text: most
bounding_box:
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top: 0.4088311791419983
width: 0.032717179507017136
height: 0.007630913984030485
confidence: 99.99
- text: of
bounding_box:
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top: 0.4073822498321533
width: 0.014926603995263577
height: 0.008962335996329784
confidence: 99.98
bounding_box:
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top: 0.40709441900253296
width: 0.3908501863479614
height: 0.011937189847230911
confidence: 99.95
- text: ASCII data (machine-readable characters). Character
words:
- text: ASCII
bounding_box:
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top: 0.42126160860061646
width: 0.04385245218873024
height: 0.0087547916918993
confidence: 99.42
- text: data
bounding_box:
left: 0.14899054169654846
top: 0.4213480055332184
width: 0.027945101261138916
height: 0.008610328659415245
confidence: 99.95
- text: (machine-readable
bounding_box:
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top: 0.42129987478256226
width: 0.12450381368398666
height: 0.010592425242066383
confidence: 99.07
- text: characters).
bounding_box:
left: 0.3502521812915802
top: 0.4212513864040375
width: 0.07620798796415329
height: 0.010675926692783833
confidence: 91.46
- text: Character
bounding_box:
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top: 0.42188575863838196
width: 0.06667636334896088
height: 0.008227386511862278
confidence: 99.76
bounding_box:
left: 0.0806555226445198
top: 0.4207415282726288
width: 0.41463345289230347
height: 0.011564141139388084
confidence: 97.93
- text: these.
words:
- text: these.
bounding_box:
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width: 0.03814553841948509
height: 0.008720058016479015
confidence: 99.89
bounding_box:
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confidence: 99.89
- text: recognition also popularly referred as optical character
words:
- text: recognition
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- text: also
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- text: popularly
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confidence: 99.91
- text: referred
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- text: as
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- text: optical
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- text: character
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bounding_box:
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width: 0.4141657054424286
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confidence: 99.95
- text: 2. Like any image, visual characters are subject to spoilage
words:
- text: '2.'
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- text: Like
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- text: any
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- text: image,
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- text: visual
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confidence: 99.88
- text: characters
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- text: are
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confidence: 99.99
- text: subject
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- text: to
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confidence: 99.98
- text: spoilage
bounding_box:
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confidence: 99.52
bounding_box:
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confidence: 99.87
- text: recognition (OCR) is a field of research that has immense
words:
- text: recognition
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confidence: 99.91
- text: (OCR)
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confidence: 99.93
- text: is
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confidence: 99.98
- text: a
bounding_box:
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width: 0.007611827924847603
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confidence: 99.93
- text: field
bounding_box:
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confidence: 99.98
- text: of
bounding_box:
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confidence: 99.98
- text: research
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confidence: 99.98
- text: that
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confidence: 99.99
- text: has
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confidence: 99.98
- text: immense
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confidence: 99.75
bounding_box:
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width: 0.41370889544487
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confidence: 99.94
- text: due to noise. Some images containing characters are
words:
- text: due
bounding_box:
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confidence: 99.96
- text: to
bounding_box:
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confidence: 99.99
- text: noise.
bounding_box:
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confidence: 99.62
- text: Some
bounding_box:
left: 0.6486693620681763
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confidence: 99.85
- text: images
bounding_box:
left: 0.6960338950157166
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confidence: 99.9
- text: containing
bounding_box:
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confidence: 99.97
- text: characters
bounding_box:
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confidence: 99.88
- text: are
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confidence: 99.98
bounding_box:
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width: 0.3896031379699707
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confidence: 99.89
- text: potential in future where we want to track and locate every
words:
- text: potential
bounding_box:
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confidence: 99.83
- text: in
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confidence: 99.97
- text: future
bounding_box:
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confidence: 99.99
- text: where
bounding_box:
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width: 0.040707122534513474
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confidence: 99.98
- text: we
bounding_box:
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confidence: 99.87
- text: want
bounding_box:
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top: 0.4645315706729889
width: 0.032503146678209305
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confidence: 99.95
- text: to
bounding_box:
left: 0.32189303636550903
top: 0.46439316868782043
width: 0.013159025460481644
height: 0.0076905349269509315
confidence: 99.98
- text: track
bounding_box:
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width: 0.03387381508946419
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confidence: 99.99
- text: and
bounding_box:
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top: 0.4630912244319916
width: 0.024096639826893806
height: 0.00893262680619955
confidence: 99.99
- text: locate
bounding_box:
left: 0.41119813919067383
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width: 0.039363984018564224
height: 0.008765753358602524
confidence: 99.91
- text: every
bounding_box:
left: 0.4567911922931671
top: 0.46549490094184875
width: 0.03713884949684143
height: 0.008580345660448074
confidence: 99.96
bounding_box:
left: 0.08059744536876678
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width: 0.41333258152008057
height: 0.012099224142730236
confidence: 99.95
- text: already blurred or not clear which makes them difficult to
words:
- text: already
bounding_box:
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top: 0.46300357580184937
width: 0.04921511560678482
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confidence: 99.98
- text: blurred
bounding_box:
left: 0.5963202118873596
top: 0.4629361629486084
width: 0.048382923007011414
height: 0.009067071601748466
confidence: 99.96
- text: or
bounding_box:
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top: 0.46549731492996216
width: 0.014059096574783325
height: 0.0064526512287557125
confidence: 99.94
- text: not
bounding_box:
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width: 0.021457698196172714
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confidence: 99.98
- text: clear
bounding_box:
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top: 0.46308326721191406
width: 0.03294210880994797
height: 0.008881552144885063
confidence: 99.97
- text: which
bounding_box:
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width: 0.040579039603471756
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confidence: 99.99
- text: makes
bounding_box:
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confidence: 99.97
- text: them
bounding_box:
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width: 0.03350303694605827
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confidence: 99.99
- text: difficult
bounding_box:
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width: 0.054010845720767975
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confidence: 99.92
- text: to
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width: 0.012951004318892956
height: 0.007326322607696056
confidence: 99.95
bounding_box:
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width: 0.38934609293937683
height: 0.011638625524938107
confidence: 99.96
- text: piece of information being exchanged. The problem with the
words:
- text: piece
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width: 0.035501476377248764
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confidence: 99.88
- text: of
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confidence: 99.96
- text: information
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confidence: 99.96
- text: being
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confidence: 99.99
- text: exchanged.
bounding_box:
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confidence: 99.04
- text: The
bounding_box:
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confidence: 99.99
- text: problem
bounding_box:
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confidence: 99.87
- text: with
bounding_box:
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width: 0.02986893057823181
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confidence: 99.99
- text: the
bounding_box:
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confidence: 99.99
bounding_box:
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width: 0.41353434324264526
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confidence: 99.85
- text: process. Noise consists of random changes to a pattern,
words:
- text: process.
bounding_box:
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- text: Noise
bounding_box:
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confidence: 99.96
- text: consists
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confidence: 99.96
- text: of
bounding_box:
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confidence: 99.98
- text: random
bounding_box:
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width: 0.05162683501839638
height: 0.00884511973708868
confidence: 99.84
- text: changes
bounding_box:
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confidence: 99.94
- text: to
bounding_box:
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width: 0.013288520276546478
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confidence: 99.99
- text: a
bounding_box:
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confidence: 99.93
- text: pattern,
bounding_box:
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width: 0.05067116767168045
height: 0.009907898493111134
confidence: 99.87
bounding_box:
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width: 0.3895138204097748
height: 0.012121865525841713
confidence: 99.94
- text: hand written text is due to uncertainties such as variation in
words:
- text: hand
bounding_box:
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confidence: 99.98
- text: written
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confidence: 99.94
- text: text
bounding_box:
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confidence: 99.97
- text: is
bounding_box:
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confidence: 99.98
- text: due
bounding_box:
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confidence: 99.95
- text: to
bounding_box:
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top: 0.49201908707618713
width: 0.013140605762600899
height: 0.007558789104223251
confidence: 99.98
- text: uncertainties
bounding_box:
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width: 0.08565919101238251
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confidence: 99.26
- text: such
bounding_box:
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confidence: 99.99
- text: as
bounding_box:
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top: 0.49316585063934326
width: 0.013426585122942924
height: 0.006350527983158827
confidence: 99.97
- text: variation
bounding_box:
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top: 0.490853875875473
width: 0.059856999665498734
height: 0.008598127402365208
confidence: 99.87
- text: in
bounding_box:
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confidence: 99.97
bounding_box:
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width: 0.41369137167930603
height: 0.009959806688129902
confidence: 99.9
- text: particularly near the edges. A character with much noise
words:
- text: particularly
bounding_box:
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width: 0.07784475386142731
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- text: near
bounding_box:
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width: 0.02928052470088005
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confidence: 99.97
- text: the
bounding_box:
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width: 0.02036236971616745
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confidence: 99.99
- text: edges.
bounding_box:
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top: 0.49095839262008667
width: 0.041173774749040604
height: 0.0110236881300807
confidence: 98.5
- text: A
bounding_box:
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width: 0.01192388590425253
height: 0.008463606238365173
confidence: 99.93
- text: character
bounding_box:
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top: 0.4908440411090851
width: 0.06225202605128288
height: 0.008707835339009762
confidence: 99.95
- text: with
bounding_box:
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top: 0.4907379448413849
width: 0.029584523290395737
height: 0.00882993545383215
confidence: 99.99
- text: much
bounding_box:
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width: 0.03703819215297699
height: 0.008677976205945015
confidence: 99.99
- text: noise
bounding_box:
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top: 0.4908142685890198
width: 0.03542392700910568
height: 0.008773384615778923
confidence: 99.88
bounding_box:
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top: 0.4903266429901123
width: 0.3897112309932709
height: 0.011857731267809868
confidence: 99.79
- text: calligraphy over period of time, similarity in text, variation
words:
- text: calligraphy
bounding_box:
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width: 0.07595489174127579
height: 0.011502011679112911
confidence: 99.87
- text: over
bounding_box:
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top: 0.5071432590484619
width: 0.030429063364863396
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confidence: 99.96
- text: period
bounding_box:
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width: 0.04272586107254028
height: 0.011513936333358288
confidence: 99.97
- text: of
bounding_box:
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top: 0.5044984817504883
width: 0.015092066489160061
height: 0.00905435997992754
confidence: 99.97
- text: time,
bounding_box:
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width: 0.033404674381017685
height: 0.010400606319308281
confidence: 99.7
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- text: may be interpreted as a completely different character by
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- text: may
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- text: in styles of writing [3] The character recognition system
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- text: '[3]'
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- text: recognition
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- text: a computer program.
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- text: helps in making the communication between a human and a
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- text: communication
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- text: between
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- text: 3. There are no hard-and-fast rules that define the
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- text: define
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- text: computer easy.[4] The character recognition is basically
words:
- text: computer
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- text: easy.[4]
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- text: The
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- text: character
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- text: recognition
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- text: is
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- text: basically
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- text: appearance of a visual character. Hence rules need to be
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- text: appearance
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- text: character.
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- text: Hence
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- text: rules
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- text: need
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- text: 'classified into two types: offline handwritten text'
words:
- text: classified
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- text: two
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- text: 'types:'
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- text: handwritten
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- text: heuristically deduced from the samples.
words:
- text: heuristically
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- text: samples.
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- text: recognition, online handwritten text recognition. Offline
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- text: online
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- text: recognition.
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- text: Offline
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- text: means the text written on the plain paper or sheet and then
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- text: means
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- text: 'on'
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- text: then
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- text: 4. Phases of OCR
words:
- text: '4.'
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- text: Phases
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- text: OCR
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- text: the writing is usually captured optically by a scanner and the
words:
- text: the
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- text: writing
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- text: Data Acquisition
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- text: strokes made by the writer are also available.[6]
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- text: available.[6]
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- text: Segmentation
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- text: 2. Applications
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- text: The area of OCR is becoming an integral part of document
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- text: Feature Extraction
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- text: scanners, and is used in many applications such as postal
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- text: depending on OCR systems to eliminate the human
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- text: Volume 2 Issue 5, May 2013
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- text: Most Important initial phase in OCR is to gather the image
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- text: characters directly for offline recognition.
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- text: radyape
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- text: In Image acquisition, the recognition system acquires a
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- text: In
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- text: scanned image as an input image. The image should have a
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- text: suitable digital input device. Data samples for the
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- text: 2. Pre Processing
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- text: characters which are ready to pass through feature extraction
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- text: stroke of characters is crucial for the success of efficient
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- text: size depending on the methods used. The segmentation
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- text: character recognition systems. Thus, preprocessing is an
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- text: essential stage prior to feature extraction since it controls the
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- text: suitability of the results for the successive stages [2].
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- text: '[2].'
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- text: matrix. These matrices are then generally normalized by
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- text: the
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- text: redundant
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- text: information
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- text: from the image without losing any important information.
words:
- text: from
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- text: losing
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- text: any
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- text: important
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- text: information.
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- text: Preprocessing can be done through various ways
words:
- text: Preprocessing
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- text: can
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- text: be
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- text: done
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- text: through
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- text: various
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- text: ways
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- text: Binarization, Noise reduction, Stroke width normalization,
words:
- text: Binarization,
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- text: Noise
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- text: reduction,
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- text: Stroke
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- text: width
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- text: normalization,
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- text: 5. Feature Extraction
words:
- text: '5.'
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- text: Feature
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- text: Extraction
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- text: Skew correction, Slant removal, Filtering, Morphological
words:
- text: Skew
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- text: correction,
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- text: removal,
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- text: Filtering,
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- text: Morphological
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- text: Operations, Noise Modelling, Skew Normalization, Size
words:
- text: Operations,
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- text: Normalization,
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- text: Size
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- text: Feature extraction is the process of extracting the relevant
words:
- text: Feature
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- text: extraction
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- text: process
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- text: relevant
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- text: Normalization, Contour Smoothing, Compression,
words:
- text: Normalization,
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- text: Contour
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- text: Smoothing,
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- text: Compression,
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- text: features from objects/alphabets to form a feature vectors.
words:
- text: features
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- text: to
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- text: form
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- text: a
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- text: feature
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- text: vectors.
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- text: Thresholding, Thinning etc
words:
- text: Thresholding,
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- text: Thinning
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- text: These feature vectors is then used by classifiers to recognize
words:
- text: These
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- text: vectors
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- text: used
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- text: classifiers
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- text: recognize
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- text: start
words:
- text: start
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- text: the input unit with target output unit. It becomes easier for
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- text: the
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- text: unit.
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- text: It
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- text: easier
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- text: the classifier to classify between different classes by looking
words:
- text: the
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- text: Feature extraction is also defined as extracting the raw data
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- text: 'Figure 1: Slant Removal'
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- text: variability.[1]
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- text: Due to the nature of handwriting with its high degree of
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- text: difficult task. Feature extraction methods are based on 3
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- text: Global transformations and moments
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- text: 'Figure 2: Normalization of ''e'' and ''l'' as in [9]'
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- text: 'Statistical Features includes:'
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- text: 3. Segmentation
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- text: 2. Projection Histograms
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- text: 'Figure 7: Contouring'
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- text: 6 Classification
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- text: The results Classification is the last stage where we train the
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- text: 7. Post Processing
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- text: 'Figure 5: Projection Histogram'
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- text: necessary for meaningful improvements in recognition rates.
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- text: 3. Profiles
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- text: developed over the years. In this paper, I have tried to
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- text: methods related to it. Many researchers try to hybrid two or
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- text: methods
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- text: more different methods and compare the results for
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- text: more
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- text: efficiency but again this will be application specific and
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- text: parameter specific. OCR has been implemented in various
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- text: parameter
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- text: countries for recognizing different languages as well.
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- text: countries
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- text: References
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- text: References
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- text: '[1] Oivind Due Trier, Anil K. Jain, Torfinn Taxt, "Feature'
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- text: Extraction Methods for Character Recognition-A
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- text: Recognition-A
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- text: Survey", July 1995
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- text: 'Figure 6: Profiling'
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- text: '[2] Yasser Alginahi, Taibah University Kingdom of Saudi'
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height: 0.01173870824277401
confidence: 99.9
- text: System
bounding_box:
left: 0.3095129430294037
top: 0.3818371295928955
width: 0.04998968169093132
height: 0.011681037954986095
confidence: 99.94
- text: Integrating
bounding_box:
left: 0.3643004894256592
top: 0.38181784749031067
width: 0.07399854809045792
height: 0.011809168383479118
confidence: 99.85
- text: Feature
bounding_box:
left: 0.4434643089771271
top: 0.3818458318710327
width: 0.05088312178850174
height: 0.009341135621070862
confidence: 99.94
bounding_box:
left: 0.11059059202671051
top: 0.3811143934726715
width: 0.3837616741657257
height: 0.013040214776992798
confidence: 99.93
- text: Volume 2 Issue 5, May 2013
words:
- text: Volume
bounding_box:
left: 0.3858547806739807
top: 0.9439122676849365
width: 0.06748292595148087
height: 0.011216918006539345
confidence: 99.88
- text: '2'
bounding_box:
left: 0.4579980671405792
top: 0.944149374961853
width: 0.010742440819740295
height: 0.010665914975106716
confidence: 99.79
- text: Issue
bounding_box:
left: 0.47243136167526245
top: 0.9440219402313232
width: 0.04490547627210617
height: 0.011099353432655334
confidence: 99.91
- text: 5,
bounding_box:
left: 0.5219507813453674
top: 0.9441676139831543
width: 0.015172993764281273
height: 0.01283935271203518
confidence: 99.74
- text: May
bounding_box:
left: 0.5416243076324463
top: 0.9439688324928284
width: 0.0399249829351902
height: 0.013766992837190628
confidence: 99.97
- text: '2013'
bounding_box:
left: 0.586654782295227
top: 0.9443179965019226
width: 0.039332110434770584
height: 0.010750086978077888
confidence: 99.91
bounding_box:
left: 0.3858547806739807
top: 0.9436088800430298
width: 0.24013793468475342
height: 0.014399330131709576
confidence: 99.87
- text: www.ijsr.net
words:
- text: www.ijsr.net
bounding_box:
left: 0.4513218104839325
top: 0.9610227942466736
width: 0.10993301868438721
height: 0.012398925609886646
confidence: 98.88
bounding_box:
left: 0.4513218104839325
top: 0.9610227942466736
width: 0.10993301868438721
height: 0.012398925609886646
confidence: 98.88
- text: '158'
words:
- text: '158'
bounding_box:
left: 0.8567489385604858
top: 0.9581685066223145
width: 0.02231697179377079
height: 0.00812011118978262
confidence: 94.95
bounding_box:
left: 0.8567489385604858
top: 0.9581685066223145
width: 0.02231697179377079
height: 0.00812011118978262
confidence: 94.95
number_of_pages: 4
summary: Response Example
description: ''
'400':
content:
application/json:
schema:
$ref: '#/components/schemas/BadRequest'
description: ''
'500':
content:
application/json:
schema:
$ref: '#/components/schemas/Error'
description: ''
'403':
content:
application/json:
schema:
$ref: '#/components/schemas/Error'
description: ''
'404':
content:
application/json:
schema:
$ref: '#/components/schemas/NotFoundResponse'
description: ''
components:
schemas:
ocrocr_asyncOcrAsyncDataClass:
properties:
raw_text:
title: Raw Text
type: string
pages:
description: List of pages
items:
$ref: '#/components/schemas/Page'
title: Pages
type: array
number_of_pages:
description: Number of pages in the document
title: Number Of Pages
type: integer
original_response:
default: null
description: original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request
title: Original Response
id:
title: Id
type: string
final_status:
allOf:
- $ref: '#/components/schemas/FinalStatusEnum'
title: Final Status
error:
additionalProperties: true
default: null
title: Error
type: object
required:
- raw_text
- number_of_pages
- id
- final_status
title: ocrocr_asyncOcrAsyncDataClass
type: object
ListAsyncJobResponse:
type: object
properties:
jobs:
type: array
items:
$ref: '#/components/schemas/AsyncJobList'
required:
- jobs
NestedError:
type: object
properties:
type:
type: string
message:
type: string
required:
- message
- type
BoundingBox:
description: "Bounding box of a word in the image\n\nAttributes:\n left (float): Left coordinate of the bounding box\n top (float): Top coordinate of the bounding box\n width (float): Width of the bounding box\n height (float): Height of the bounding box\n text (str): Text detected in the bounding box\n\nConstructor:\n from_json (classmethod): Create a new instance of BoundingBox from a JSON object\n from_normalized_vertices (classmethod): Create a new instance of BoundingBox from normalized vertices\n unknown (classmethod): Return a invalid bouding_box with all field filled with `-1`"
properties:
left:
description: Left coordinate of the bounding box
title: Left
type: integer
top:
description: Top coordinate of the bounding box
title: Top
type: integer
width:
description: Width of the bounding box
title: Width
type: integer
height:
description: Height of the bounding box
title: Height
type: integer
required:
- left
- top
- width
- height
title: BoundingBox
type: object
asyncocrocr_asyncResponseModel:
properties:
results:
$ref: '#/components/schemas/ocrocr_asyncModel'
error:
title: Error
type: string
public_id:
format: uuid
title: Public Id
type: string
status:
title: Status
type: string
required:
- results
- error
- public_id
- status
title: asyncocrocr_asyncResponseModel
type: object
NotFoundResponse:
type: object
properties:
details:
type: string
default: Not Found
FinalStatusEnum:
enum:
- sucess
- fail
type: string
AsyncJobList:
type: object
properties:
providers:
type: string
nb:
type: integer
nb_ok:
type: integer
public_id:
type: string
format: uuid
state:
$ref: '#/components/schemas/StateEnum'
created_at:
type: string
format: date-time
required:
- created_at
- nb
- nb_ok
- providers
- public_id
- state
ocrocr_asyncModel:
properties:
oneai:
$ref: '#/components/schemas/ocrocr_asyncOcrAsyncDataClass'
default: null
amazon:
$ref: '#/components/schemas/ocrocr_asyncOcrAsyncDataClass'
default: null
microsoft:
$ref: '#/components/schemas/ocrocr_asyncOcrAsyncDataClass'
default: null
mistral:
$ref: '#/components/schemas/ocrocr_asyncOcrAsyncDataClass'
default: null
google:
$ref: '#/components/schemas/ocrocr_asyncOcrAsyncDataClass'
default: null
title: ocrocr_asyncModel
type: object
StateEnum:
enum:
- finished
- failed
- Timeout error
- processing
type: string
description: '* `finished` - finished
* `failed` - failed
* `Timeout error` - Timeout error
* `processing` - processing'
Line:
description: "Line of a document\n\nAttributes:\n text (str): Text detected in the line\n bounding_boxes (Sequence[BoundingBox]): Bounding boxes of the words in the line\n words (Sequence[Word]): List of words of the line\n confidence (float): Confidence of the line"
properties:
text:
description: Text detected in the line
title: Text
type: string
words:
description: List of words
items:
$ref: '#/components/schemas/Word'
title: Words
type: array
bounding_box:
$ref: '#/components/schemas/BoundingBox'
default: null
description: Bounding box of the line, can be None
confidence:
description: Confidence of the line
title: Confidence
type: integer
required:
- text
- confidence
title: Line
type: object
NestedBadRequest:
type: object
properties:
type:
type: string
message:
$ref: '#/components/schemas/FieldError'
required:
- message
- type
LaunchAsyncJobResponse:
type: object
properties:
public_id:
type: string
format: uuid
required:
- public_id
AsyncOcrRequest:
type: object
properties:
settings:
type: string
default: {}
description: "A dictionnary or a json object to specify specific models to use for some providers.
It can be in the following format: {\"google\" : \"google_model\", \"ibm\": \"ibm_model\"...}.\n "
providers:
type: array
items:
type: string
minLength: 1
description: 'It can be one (ex: **''amazon''** or **''google''**) or multiple provider(s) (ex: **''amazon,microsoft,google''**) that the data will be redirected to in order to get the processed results.
Providers can also be invoked with specific models (ex: providers: **''amazon/model1, amazon/model2, google/model3''**)'
fallback_providers:
type: array
items:
type: string
default: []
description: "Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.\n To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.\n\nThey will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.\n\n\n*Doesn't work with async subfeatures.*\n "
maxItems: 5
show_original_response:
type: boolean
default: false
description: "Optional : Shows the original response of the provider.
\n When set to **true**, a new attribute *original_response* will appear in the response object."
webhook_receiver:
type: string
format: uri
minLength: 1
description: 'Webhook receiver should be a valid https URL (ex : https://your.listner.com/endpoint). After the processing is done, the webhook endpoint will receive a POST request with the result.'
users_webhook_parameters:
description: 'Json data that contains of additional parameters that will be sent back to the webhook receiver (ex: api key for security or client''s data ID to link the result internally). Will only be used when webhook_receiver is set.'
send_webhook_data:
type: boolean
default: true
description: If set to false the webhook will not contain the result data. Use if your webhook receiver has a request size limit.
show_base_64:
type: boolean
default: true
file:
type: string
format: binary
description: 'File to analyse in binary format to be used with *content-type*: **multipart/form-data**
**Does not work with application/json !**'
file_url:
type:
- string
- 'null'
format: uri
description: 'File **URL** to analyse to be used with with *content-type*: **application/json**.'
file_password:
type:
- string
- 'null'
description: If your PDF file has a password, you can pass it here!
maxLength: 200
required:
- providers
FieldError:
type: object
properties:
:
type: array
items:
type: string
required:
-
Error:
type: object
properties:
error:
$ref: '#/components/schemas/NestedError'
required:
- error
BadRequest:
type: object
properties:
error:
$ref: '#/components/schemas/NestedBadRequest'
required:
- error
Page:
description: "Page of a document\n\nAttributes:\n lines (Sequence[Line]): List of lines of the page"
properties:
lines:
description: List of lines
items:
$ref: '#/components/schemas/Line'
title: Lines
type: array
title: Page
type: object
Word:
description: "Word of a document\n\nAttributes:\n text (str): Text detected in the word\n bounding_boxes (Sequence[BoundingBox]): Bounding boxes of the words in the word\n confidence (float): Confidence score of the word"
properties:
text:
description: Text detected in the word
title: Text
type: string
bounding_box:
$ref: '#/components/schemas/BoundingBox'
description: Bounding boxes of the words in the word
confidence:
description: Confidence score of the word
title: Confidence
type: integer
required:
- text
- bounding_box
- confidence
title: Word
type: object
securitySchemes:
FeatureApiAuth:
type: http
scheme: bearer
bearerFormat: JWT