# Scene-Text-Understanding

## Survey

- [2015-PAMI] Text Detection and Recognition in Imagery: A Survey [`paper`](http://lampsrv02.umiacs.umd.edu/pubs/Papers/qixiangye-14/qixiangye-14.pdf)
- [2014-Front.Comput.Sci] Scene Text Detection and Recognition: Recent Advances and Future Trends [`paper`](http://mc.eistar.net/uploadfiles/Papers/FCS_TextSurvey_2015.pdf)
- [2020-Arxiv] Text Recognition in the Wild: A survey[`paper`](https://arxiv.org/pdf/2005.03492.pdf)

## Scene Text Detection
- [2019-CVPR] Arbitrary Shape Scene Text Detection with Adaptive Text Region Representation [[`paper`]](https://arxiv.org/abs/1905.05980)
- [2019-CVPR] A Multitask Network for Localization and Recognition of Text in Images(end-to-end) [[`paper`]](https://arxiv.org/abs/1906.09266)
- [2019-CVPR] AFDM: Handwriting Recognition in Low-resource Scripts using Adversarial Learning(data augmentation) [[`paper`]](https://arxiv.org/abs/1811.01396) [[`code`]](https://github.com/AyanKumarBhunia/Handwriting_Recogition_using_Adversarial_Learning)
- [2019-CVPR] CRAFT: Character Region Awareness for Text Detection [[`paper`]](https://arxiv.org/abs/1904.01941) [[`code`]](https://github.com/clovaai/CRAFT-pytorch)
- [2019-CVPR] Data Extraction from Charts via Single Deep Neural Network(*) [[`paper`]](https://arxiv.org/abs/1906.11906?from=singlemessage&isappinstalled=0)
- [2019-CVPR] E2E-MLT - an Unconstrained End-to-End Method for Multi-Language Scene Text [[`paper`]](https://arxiv.org/abs/1801.09919)
- [2019-arXiv] FACLSTM: ConvLSTM with Focused Attention for Scene Text Recognition [[`paper`]](https://arxiv.org/abs/1904.09405)
- [2019-CVPR] Look More Than Once: An Accurate Detector for Text of Arbitrary Shapes [[`paper`]](https://arxiv.org/abs/1904.06535)
- [2019-CVPR] PSENET: Shape Robust Text Detection with Progressive Scale Expansion Network [[`paper`]](https://arxiv.org/abs/1903.12473)[[tensorflow]](https://github.com/liuheng92/tensorflow_PSENet)[[Pytorch]](https://github.com/whai362/PSENet)
- [2019-CVPR] PMTD: Pyramid Mask Text Detector [[`paper`]](https://arxiv.org/abs/1903.11800) [[`code`]](https://github.com/STVIR/PMTD)
- [2019-CVPR] Spatial Fusion GAN for Image Synthesis (word Synthesis) [[`paper`]](https://arxiv.org/abs/1812.05840 [[`code`]](https://github.com/Sunshine352/SF-GAN)
- [2019-CVPR] Scene Text Detection with Supervised Pyramid Context Network [[`paper`]](https://arxiv.org/abs/1811.08605)[[keras]](https://github.com/AirBernard/Scene-Text-Detection-with-SPCNET)
- [2019-arXiv] TextField: Learning A Deep Direction Field for Irregular Scene Text Detection [[`paper`]](https://arxiv.org/abs/1812.01393) [[`code`]](https://github.com/YukangWang/TextField)
- [2019-CVPR] Typography with Decor: Intelligent Text Style Transfer [[`paper`]](https://github.com/daooshee/Typography2019/blob/master/3159.pdf) [[`code`]](https://github.com/daooshee/Typography-with-Decor)
- [2019-CVPR] TIOU: Tightness-aware Evaluation Protocol for Scene Text Detection(new Evalution tool)[[`paper`]](https://arxiv.org/abs/1904.00813) [[`code`]](https://github.com/Yuliang-Liu/TIoU-metric)
- [2019-arXiv] MORAN: A Multi-Object Rectified Attention Network for Scene Text Recognition [[`paper`]](https://arxiv.org/abs/1901.03003) [[`code`]](https://github.com/Canjie-Luo/MORAN_v2)
- [2019-CVPR] Scene Text Magnifier [[`paper`]](https://arxiv.org/abs/1907.00693)
- [2018-CVPR] Pixel-Anchor: A Fast Oriented Scene Text Detector with Combined Networks [[`paper`]](https://arxiv.org/abs/1811.07432)
- [2018-ECCV] Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes [[`paper`]](https://arxiv.org/abs/1807.02242) [[`code`]](https://github.com/lvpengyuan/masktextspotter.caffe2)
- [2018-AAAI] PixelLink: Detecting Scene Text via Instance Segmentation [[`paper`]](https://arxiv.org/abs/1801.01315) [[`code`]](https://github.com/ZJULearning/pixel_link)
- [2018-CVPR] RRPN: Arbitrary-Oriented Scene Text Detection via Rotation Proposals [[`paper`]](https://arxiv.org/pdf/1703.01086.pdf) [[`code`]](http://https//github.com/DetectionTeamUCAS/RRPN_Faster-RCNN_Tensorflow) 
- [2018-CPVR] Multi-Oriented Scene Text Detection via Corner Localization and Region Segmentation [[Paper]](https://arxiv.org/pdf/1802.08948.pdf)
- [2018-arxiv] PixelLink: Detecting Scene Text via Instance Segmentation [[Paper]](https://arxiv.org/pdf/1801.01315.pdf)
- [2018-AAAI] SEE: Towards Semi-Supervised End-to-End Scene Text Recognition [[Paper]](https://arxiv.org/abs/1712.05404)
- [2018-arxiv] TextBoxes++: A Single-Shot Oriented Scene Text Detector[[Paper]](https://arxiv.org/pdf/1801.02765.pdf)
- [2017-arxiv] Attention-based Extraction of Structured [[Paper]](https://arxiv.org/pdf/1704.03549.pdf)
- [2017-ICCV]Single Shot TextDetector with Regional Attention [[Paper]](https://arxiv.org/pdf/1709.00138.pdf)
- [2017-ICCV]WordSup: Exploiting Word Annotations for Character based Text Detection [[Paper]](https://arxiv.org/pdf/1708.06720.pdf)
- [2017-arXiv]R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection[[Paper]](https://arxiv.org/ftp/arxiv/papers/1706/1706.09579.pdf)
- [2017-CVPR]EAST: An Efficient and Accurate Scene Text Detector [[Paper]](https://arxiv.org/abs/1704.03155) [[Code]](https://github.com/argman/EAST)
- [2017-arXiv]Cascaded Segmentation-Detection Networks for Word-Level Text Spotting[[Paper]](https://arxiv.org/abs/1704.00834)
- [2017-arXiv]Deep Direct Regression for Multi-Oriented Scene Text Detection [[Paper]](https://arxiv.org/pdf/1703.08289.pdf)
- [2017-CVPR]Detecting oriented text in natural images by linking segments [[Paper]](http://mc.eistar.net/UpLoadFiles/Papers/SegLink_CVPR17.pdf)
- [2017-CVPR]Deep Matching Prior Network: Toward Tighter Multi-oriented Text Detection [[Paper]](https://arxiv.org/pdf/1703.01425.pdf)
- [2017-arXiv]Arbitrary-Oriented Scene Text Detection via Rotation Proposals [[Paper]](https://arxiv.org/pdf/1703.01086.pdf)
- [2017-AAAI]TextBoxes: A Fast Text Detector with a Single Deep Neural Network[[Paper]](https://arxiv.org/abs/1611.06779)[[Code]](https://github.com/MhLiao/TextBoxes)
- [2016-arXiv]Accurate Text Localization in Natural Image with Cascaded Convolutional TextNetwork  [[Paper]](http://arxiv.org/abs/1603.09423)
- [2016-arXiv]DeepText : A Unified Framework for Text Proposal Generation and Text Detectionin Natural Images  [[Paper]](http://arxiv.org/abs/1605.07314) [[Data]](https://www.dropbox.com/s/06wfn5ugt5v3djs/SCUT_FORU_DB_Release.rar?dl=0)
- [2017-PR]TextProposals: a Text-specific Selective Search Algorithm for Word Spotting in the Wild [[paper]](https://arxiv.org/pdf/1604.02619.pdf) [[code]](https://github.com/lluisgomez/TextProposals)
- [2016-arXiv] Scene Text Detection via Holistic, Multi-Channel Prediction [[Paper]](https://arxiv.org/pdf/1606.09002.pdf)
- [2016-CVPR] CannyText Detector: Fast and Robust Scene Text Localization Algorithm [[Paper]](http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Cho_Canny_Text_Detector_CVPR_2016_paper.pdf)
- [2016-CVPR]Synthetic Data for Text Localisation in Natural Images[[Paper]](http://www.robots.ox.ac.uk/~vgg/data/scenetext/gupta16.pdf) [[Data]](http://www.robots.ox.ac.uk/~vgg/data/scenetext/) [[Code]](https://github.com/ankush-me/SynthText)
- [2016-ECCV]Detecting Text in Natural Image with Connectionist Text Proposal Network[[Paper]](https://arxiv.org/pdf/1609.03605v1.pdf) [[Demo]](http://textdet.com/)[[Code]](https://github.com/tianzhi0549/CTPN)
- [2016-TIP]Text-Attentional Convolutional Neural Networks for Scene Text Detection[[Paper]](http://whuang.org/papers/the2016_tip.pdf)
- [2016-IJDAR]TextCatcher: a method to detect curved and challenging text in natural scenes[[Paper]](http://rd.springer.com/article/10.1007/s10032-016-0264-4)
- [2016-CVPR]Multi-oriented text detection with fully convolutional networks[[Paper]](http://mclab.eic.hust.edu.cn/UpLoadFiles/Papers/TextDectionFCN_CVPR16.pdf)
- [2015-TPRMI]Real-time Lexicon-free Scene Text Localization and Recognition
- [2015-CVPR]Symmetry-Based Text Line Detection in Natural Scenes
- [2015-ICCV]FASText: Efficient unconstrained scene text detector [[Paper]](https://pdfs.semanticscholar.org/2131/106318d4674bc9260e671c9f427bfc3f1029.pdf) https://github.com/MichalBusta/FASText
- [2015-D.PhilThesis] Deep Learning for Text Spotting  [[Paper]](http://www.robots.ox.ac.uk/~vgg/publications/2015/Jaderberg15b/jaderberg15b.pdf)
- [2015 ICDAR]Object Proposals for Text Extraction in the Wild [[Paper]](http://arxiv.org/abs/1509.02317) https://github.com/lluisgomez/TextProposals
- [2014-ECCV] Deep Features for Text Spotting  [[Paper]](http://www.robots.ox.ac.uk/~vgg/publications/2014/Jaderberg14/jaderberg14.pdf) https://bitbucket.org/jaderberg/eccv2014_textspotting https://bitbucket.org/jaderberg/eccv2014_textspotting http://gitxiv.com/posts/uB4y7QdD5XquEJ69c/deep-features-for-text-spotting
- [2014-TPAMI] Word Spotting and Recognition with Embedded Attributes [[Paper]](http://www.cvc.uab.es/~afornes/publi/journals/2014_PAMI_Almazan.pdf) http://www.cvc.uab.es/~almazan/index/projects/words-att/index.html  https://github.com/almazan/watts
- [2014-TPRMI]Robust Text Detection in Natural Scene Images
- [2014-ECCV] Robust Scene Text Detection with Convolution Neural Network Induced MSER Trees [[Paper]](http://www.whuang.org/papers/whuang2014_eccv.pdf)
- [2013-ICCV] Photo OCR: Reading Text in Uncontrolled Conditions  [[Paper]](https://ai2-s2-pdfs.s3.amazonaws.com/31a8/803d7e2618bfa44c472d003055bb5961b9de.pdf)
- [2012-CVPR]Real-time scene text localization and recognition [[Paper]](http://cmp.felk.cvut.cz/~matas/papers/neumann-2012-rt_text-cvpr.pdf)
- [2010-CVPR]Detecting Text in Natural Scenes with Stroke Width Transform [[Paper]](http://www.math.tau.ac.il/~turkel/imagepapers/text_detection.pdf)

### Scene Text Recognition
- [2019-CVPR] ESIR: End-to-end Scene Text Recognition via Iterative Image Rectification [[`paper`]](https://arxiv.org/abs/1812.05824) [[`code`]](https://github.com/fnzhan/ESIR) [[`code`]](https://github.com/MichalBusta/E2E-MLT)
- [2019-CVPR] E2E-MLT: an Unconstrained End-to-End Method for Multi-Language Scene Text [[`paper`]](https://arxiv.org/abs/1801.09919)
- [2018-CVPR] FOTS: Fast  [[`paper`]](https://arxiv.org/abs/1801.01671)
- [2017-ICCV] WeText: Scene Text Detection under Weak Supervision [[Paper]](http://openaccess.thecvf.com/content_ICCV_2017/papers/Tian_WeText_Scene_Text_ICCV_2017_paper.pdf)
- [2017-ICCV] Single Shot Text Detector with Regional Attention [[Paper]](http://openaccess.thecvf.com/content_ICCV_2017/papers/He_Single_Shot_Text_ICCV_2017_paper.pdf) [[Code]](https://github.com/BestSonny/SSTD)
- [2017-ICCV] Self-organized Text Detection with Minimal Post-processing via Border Learning [[Paper]](http://openaccess.thecvf.com/content_ICCV_2017/papers/Wu_Self-Organized_Text_Detection_ICCV_2017_paper.pdf)
- [2017-ICCV] Focusing Attention: Towards Accurate Text Recognition in Natural Images [[Paper]](http://openaccess.thecvf.com/content_ICCV_2017/papers/Cheng_Focusing_Attention_Towards_ICCV_2017_paper.pdf)
- [2017-ICCV] Towards End-to-end Text Spotting with Convolutional Recurrent Neural Networks [[Paper]](http://openaccess.thecvf.com/content_ICCV_2017/papers/Li_Towards_End-To-End_Text_ICCV_2017_paper.pdf)
- [2017-CVPR] Unambiguous Text Localization and Retrieval for Cluttered Scenes [[Paper]](http://openaccess.thecvf.com/content_cvpr_2017/papers/Rong_Unambiguous_Text_Localization_CVPR_2017_paper.pdf)
- [2017-ICCV] WordSup: Exploiting Word Annotations for Character based Text Detection [[Paper]](http://openaccess.thecvf.com/content_ICCV_2017/papers/Hu_WordSup_Exploiting_Word_ICCV_2017_paper.pdf)
- [2017-ICCV] Deep TextSpotter: An End-to-End Trainable Scene Text Localization and
Recognition Framework [[Paper]](http://openaccess.thecvf.com/content_ICCV_2017/papers/Busta_Deep_TextSpotter_An_ICCV_2017_paper.pdf) [[Code]](https://github.com/MichalBusta/DeepTextSpotter)
- [2017-arXiv] Cascaded Segmentation-Detection Networks for Word-Level Text Spotting [[Paper]](https://arxiv.org/abs/1704.00834)
- [2017-AAAI] Detection and Recognition of Text Embedding in Online Images via Neural Context Models [[Paper]](https://www.google.com.hk/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&ved=0ahUKEwj0o9Cfqc3SAhVIXLwKHeSLABQQFggfMAE&url=https%3a%2f%2fraw%2egithubusercontent%2ecom%2fcmkang%2fCTSN%2fmaster%2faaai2017_cameraready%2epdf&usg=AFQjCNHcFvXZ0TDWnGnl20LD98V3LWxpbA) [[Code]](https://github.com/cmkang/CTSN)
- [2017-arXiv] Improving Text Proposal for Scene Images with Fully Convolutional Networks [[Paper]](https://arxiv.org/abs/1702.05089)
- [2017-AAAI] TextBoxes: A Fast TextDetector with a Single Deep Neural Network [[Paper]](https://arxiv.org/abs/1611.06779) [[Code]](http://mclab.eic.hust.edu.cn/~xbai/CRNN/crnn_code.zip) [`github 代码`](https://github.com/bgshih/crnn)
- [2017-CVPR] Detecting Oriented Text in Natural Images by Linking Segments [[Paper]](http://mclab.eic.hust.edu.cn/UpLoadFiles/Papers/SegLink_CVPR17.pdf)
- [2017-arXiv] Arbitrary-Oriented Scene Text Detection via Rotation Proposals [[Paper]](https://arxiv.org/abs/1703.01086)
- [2017-CVPR] Deep Matching Prior Network: Toward Tighter Multi-oriented Text Detection [[Paper]](https://arxiv.org/abs/1703.01425)
- [2016-arXiv] DeepText:A Unified Framework for Text Proposal Generation and Text Detection in Natural Images [[Paper]](http://arxiv.org/abs/1605.07314)
- [2017-arvix ] Full-Page TextRecognition : Learning Where to Start and When to Stop https://arxiv.org/pdf/1704.08628.pdf
- [2016-AAAI]Reading Scene Text in Deep Convolutional Sequences [[Paper]](http://whuang.org/papers/phe2016_aaai.pdf)
- [2016-IJCV]Reading Text in the Wild with Convolutional Neural Networks [[Paper]](http://arxiv.org/abs/1412.1842) http://zeus.robots.ox.ac.uk/textsearch/#/search/ http://www.robots.ox.ac.uk/~vgg/research/text
- [2016-CVPR]Recursive Recurrent Nets with Attention Modeling for OCR in the Wild [[Paper]](http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Lee_Recursive_Recurrent_Nets_CVPR_2016_paper.pdf)
- [2016-CVPR] Robust Scene Text Recognition with Automatic Rectification [[Paper]](http://arxiv.org/pdf/1603.03915v2.pdf)
- [2016-NIPs] Generative Shape Models: Joint Text Recognition and Segmentation with Very Little Training Data [[Paper]](https://papers.nips.cc/paper/6071-generative-shape-models-joint-text-recognition-and-segmentation-with-very-little-training-data.pdf)
- [2015-CoRR] AnEnd-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition [[Paper]](http://arxiv.org/pdf/1507.05717v1.pdf) https://github.com/bgshih/crnn
- [2015-ICDAR]Automatic Script Identification in the Wild [[Paper]](https://arxiv.org/pdf/1505.02982.pdf)
- [2015-ICLR] Deep structured output learning for unconstrained text recognition [[Paper]](http://arxiv.org/abs/1412.5903)
- [2014-NIPS]Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition [[Paper]](http://www.robots.ox.ac.uk/~vgg/publications/2014/Jaderberg14c/jaderberg14c.pdf) http://www.robots.ox.ac.uk/~vgg/publications/2014/Jaderberg14c/ http://www.robots.ox.ac.uk/~vgg/research/text/model_release.tar.gz 
- [2014-TIP] A Unified Framework for Multi-Oriented Text Detection and Recognition
- [2012-ICPR]End-to-End Text Recognition with Convolutional Neural Networks [[Paper]](http://www.cs.stanford.edu/~acoates/papers/wangwucoatesng_icpr2012.pdf)  http://cs.stanford.edu/people/twangcat/ICPR2012_code/SceneTextCNN_demo.tar http://ufldl.stanford.edu/housenumbers/

### Phd Thesis
- [2016-PhD Thesis] Context Modeling for Semantic Text Matching and Scene Text Detection [[Paper]](https://etda.libraries.psu.edu/catalog/zw12z528p)
- [2015-PhD Thesis] Deep Learning for Text Spotting
 [[Paper]](http://www.robots.ox.ac.uk/~vgg/publications/2015/Jaderberg15b/jaderberg15b.pdf)
- [2012-PhD thesis] End-to-End Text Recognition with Convolutional Neural Networks [[Paper]](http://cs.stanford.edu/people/dwu4/HonorThesis.pdf)

### Text Detection
- [2018-arxiv] TextBoxes++: A Single-Shot Oriented Scene Text Detector [[Paper]](https://arxiv.org/pdf/1801.02765.pdf)

### Dataset
[`PowerPoint Text Detection and Recognition Dataset`](https://gitlab.com/rex-yue-wu/ISI-PPT-Dataset) `2017`

[COCO-Text (ComputerVision Group, Cornell) 2016](http://vision.cornell.edu/se3/coco-text/)
-  63,686images, 173,589 text instances, 3 fine-grained text attributes.
- Task:text location and recognition

[COCO-Text API]()

[Synthetic Data for Text Localisation in Natural Image (VGG)2016]()

- 800k thousand images
- 8 million synthetic word instances
- [download]()

[Synthetic Word Dataset (Oxford, VGG) 2014](http://www.robots.ox.ac.uk/~vgg/data/text/)

- 9million images covering 90k English words
- Task:text recognition, segmentation
- [download](http://www.robots.ox.ac.uk/~vgg/data/text/mjsynth.tar.gz)

[IIIT 5K-Words 2012](http://cvit.iiit.ac.in/projects/SceneTextUnderstanding/IIIT5K.html)

- 5000images from Scene Texts and born-digital (2k training and 3k testing images)
- Eachimage is a cropped word image of scene text with case-insensitive labels
- Task:text recognition
- [download](http://cvit.iiit.ac.in/projects/SceneTextUnderstanding/IIIT5K-Word_V3.0.tar.gz)

[StanfordSynth(Stanford, AI Group) 2012](http://cs.stanford.edu/people/twangcat/#research)

- Small single-character images of 62 characters (0-9, a-z, A-Z)
- Task:text recognition
- [download](http://cs.stanford.edu/people/twangcat/ICPR2012_code/syntheticData.tar)

[MSRA Text Detection 500 Database(MSRA-TD500) 2012](http://www.iapr-tc11.org/mediawiki/index.php/MSRA_Text_Detection_500_Database_(MSRA-TD500))

- 500 natural images(resolutions of the images vary from 1296x864 to 1920x1280)
- Chinese,English or mixture of both
- Task:text detection

[Street View Text (SVT) 2010](http://tc11.cvc.uab.es/datasets/SVT_1)

- 350 high resolution images (average size 1260 × 860) (100 images for training and 250 images for testing)
- Only word level bounding boxes are provided with case-insensitive labels
- Task:text location

[KAIST Scene_Text Database 2010](http://www.iapr-tc11.org/mediawiki/index.php/KAIST_Scene_Text_Database)

- 3000 images of indoor and outdoor scenes containing text
- Korean,English (Number), and Mixed (Korean + English + Number)
- Task:text location, segmentation and recognition

[Chars74k 2009](http://www.ee.surrey.ac.uk/CVSSP/demos/chars74k/)

- Over 74K images from natural images, as well as a set of synthetically generatedcharacters
- Smallsingle-character images of 62 characters (0-9, a-z, A-Z)
- Task:text recognition

- `ICDAR Benchmark Datasets`

|Dataset| Discription | Competition Paper |
|---|---|----
|[ICDAR 2017](http://rrc.cvc.uab.es/)| 42618 training images and 9837 testing images|`paper`  [![link](https://www.lds.org/bc/content/shared/content/images/gospel-library/manual/10735/paper-icon_1150845_tmb.jpg)](https://vision.cornell.edu/se3/wp-content/uploads/2019/01/ICDAR2017b.pdf)|
|[ICDAR 2015](http://rrc.cvc.uab.es/)| 1000 training images and 500 testing images|`paper`  [![link](https://www.lds.org/bc/content/shared/content/images/gospel-library/manual/10735/paper-icon_1150845_tmb.jpg)](http://rrc.cvc.uab.es/files/Robust-Reading-Competition-Karatzas.pdf)|
|[ICDAR 2013](http://dagdata.cvc.uab.es/icdar2013competition/)| 229 training images and 233 testing images |`paper`  [![link](https://www.lds.org/bc/content/shared/content/images/gospel-library/manual/10735/paper-icon_1150845_tmb.jpg)](http://dagdata.cvc.uab.es/icdar2013competition/files/icdar2013_competition_report.pdf)|
|[ICDAR 2011](http://robustreading.opendfki.de/trac/)| 229 training images and 255 testing images |`paper`  [![link](https://www.lds.org/bc/content/shared/content/images/gospel-library/manual/10735/paper-icon_1150845_tmb.jpg)](http://www.iapr-tc11.org/archive/icdar2011/fileup/PDF/4520b491.pdf)|
|[ICDAR 2005](http://www.iapr-tc11.org/mediawiki/index.php/ICDAR_2005_Robust_Reading_Competitions)| 1001 training images and 489 testing images |`paper`  [![link](https://www.lds.org/bc/content/shared/content/images/gospel-library/manual/10735/paper-icon_1150845_tmb.jpg)](http://www.academia.edu/download/30700479/10.1.1.96.4332.pdf)|
|[ICDAR 2003](http://www.iapr-tc11.org/mediawiki/index.php/ICDAR_2003_Robust_Reading_Competitions)| 181 training images and 251 testing images(word level and character level) |`paper`  [![link](https://www.lds.org/bc/content/shared/content/images/gospel-library/manual/10735/paper-icon_1150845_tmb.jpg)](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.332.3461&rep=rep1&type=pdf)|



## Blogs
- [Scene Text Detection with OpenCV 3](http://docs.opencv.org/3.0-beta/modules/text/doc/erfilter.html)
- [Handwritten numbers detection and recognition](https://medium.com/@o.kroeger/recognize-your-handwritten-numbers-3f007cbe46ff#.8hg7vl6mo)
- [Applying OCR Technology for Receipt Recognition](http://rnd.azoft.com/applying-ocr-technology-receipt-recognition/)
- [Convolutional Neural Networks for Object(Car License) Detection](http://rnd.azoft.com/convolutional-neural-networks-object-detection/)
- [Extracting text from an image using Ocropus](http://www.danvk.org/2015/01/09/extracting-text-from-an-image-using-ocropus.html)
- [Number plate recognition with Tensorflow](http://matthewearl.github.io/2016/05/06/cnn-anpr/) [[github]](https://github.com/matthewearl/deep-anpr)
- [Using deep learning to break a Captcha system](https://deepmlblog.wordpress.com/2016/01/03/how-to-break-a-captcha-system/) [`report`](http://web.stanford.edu/~jurafsky/burszstein_2010_captcha.pdf) [[github]](https://github.com/arunpatala/captcha)
- [Breaking reddit captcha with 96% accuracy](https://deepmlblog.wordpress.com/2016/01/05/breaking-reddit-captcha-with-96-accuracy/) [[github]](https://github.com/arunpatala/reddit.captcha)
- [Scene Text Recognition in iOS](https://medium.com/@khurram.pak522/scene-text-recognition-in-ios-11-2d0df8412151) [[github]](https://github.com/khurram18/SceneTextRecognitioniOS)

## Online Service

| Name | Description |
|---|----
|[Online OCR](https://www.onlineocr.net/)| API，Free |
|[Free OCR](http://www.free-ocr.com/)| API，Free |
|[New OCR](http://www.newocr.com/)| API，Free |
|[ABBYY FineReader Online](https://finereaderonline.com)| nonAPI，free |


### Open Resources Code
- 本项目基于yolo3 与crnn 实现中文自然场景文字检测及识别 [[code]](https://github.com/chineseocr/chineseocr)
- 超轻量级中文ocr，支持竖排文字识别, 支持ncnn推理 , psenet(8.5M) + crnn(6.3M) + anglenet(1.5M) 总模型仅17M [[code]](https://github.com/ouyanghuiyu/chineseocr_lite)
- Tesseract c++ based tools for documents analysis and OCR [[code]](https://github.com/tesseract-ocr/tesseract)
- Ocropy</span><span style="background:#F2EDE1">: Python-based tools for document analysis and OCR https://github.com/tmbdev/ocropy
- CLSTM A small implementation of LSTM networks,focused on OCR https://github.com/tmbdev/clstm
- Convolutional Recurrent Neural Network Torch7 https://github.com/bgshih/crnn
- Attention-OCR Visual Attention based OCR https://github.com/da03/Attention-OCR
- Umaru: An OCR-system based on torch using the technique of LSTM/GRU-RNN, CTC and referred to the works of rnnlib and clstm https://github.com/edward-zhu/umaru
- AKSHAYUBHAT/DeepVideoAnalytics  (CTPN+CRNN) [code](https://github.com/AKSHAYUBHAT/DeepVideoAnalytics/tree/master/notebooks/OCR)
- ankush-me/SynthText [code](https://github.com/ankush-me/SynthText)
- JarveeLee/SynthText_Chinese_version [code](https://github.com/JarveeLee/SynthText_Chinese_version)

#### Hand Writing Recognition</strong>
- [2016-arXiv]Drawingand Recognizing Chinese Characters with Recurrent Neural Network  https://arxiv.org/abs/1606.06539
- Learning Spatial-Semantic Context with Fully Convolutional Recurrent Network for Online Handwritten Chinese Text Recognition  https://arxiv.org/abs/1610.02616
- Stroke Sequence-Dependent Deep Convolutional Neural Network for Online Handwritten Chinese Character Recognition  https://arxiv.org/abs/1610.04057
- High Performance Offline Handwritten Chinese Character Recognition Using GoogLeNet and Directional Feature Maps  http://arxiv.org/abs/1505.04925"> 
- DeepHCCR:Offline Handwritten Chinese Character Recognition based on GoogLeNet and AlexNet (With CaffeModel)  https://github.com/chongyangtao/DeepHCCR"> </span></p>
- Scan,Attend and Read: End-to-End Handwritten Paragraph Recognition with MDLSTMAttention  http://arxiv.org/abs/1604.03286
- MLPaint:the Real-Time Handwritten Digit Recognizer http://blog.mldb.ai/blog/posts/2016/09/mlpaint/
- caffe-ocr: OCR with caffe deep learning framework https://github.com/pannous/caffe-ocr

####  Licence Tag Recognition
- ReadingCar License Plates Using Deep Convolutional Neural Networks and LSTMs 
- Numberplate recognition with Tensorflow http://matthewearl.github.io/2016/05/06/cnn-anpr/
- end-to-end-for-plate-recognition  href="https://github.com/szad670401/end-to-end-for-chinese-plate-recognitionbhttp://rnd.azoft.com/applying-ocr-technology-receipt-recognition/