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https://github.com/chongyangtao/Awesome-Scene-Text-Recognition

A curated list of resources dedicated to scene text localization and recognition
https://github.com/chongyangtao/Awesome-Scene-Text-Recognition

List: Awesome-Scene-Text-Recognition

natural-images scene-texts text-detection text-recognition

Last synced: about 2 months ago
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A curated list of resources dedicated to scene text localization and recognition

Host: GitHub
URL: https://github.com/chongyangtao/Awesome-Scene-Text-Recognition
Owner: chongyangtao
Created: 2016-07-07T13:39:08.000Z (about 8 years ago)
Default Branch: master
Last Pushed: 2018-07-30T08:05:09.000Z (about 6 years ago)
Last Synced: 2024-05-23T04:00:50.804Z (4 months ago)
Topics: natural-images, scene-texts, text-detection, text-recognition
Homepage:
Size: 21.5 KB
Stars: 1,665
Watchers: 151
Forks: 412
Open Issues: 4
Metadata Files:
- Readme: README.md

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ultimate-awesome - Awesome-Scene-Text-Recognition - A curated list of resources dedicated to scene text localization and recognition. (Other Lists / PowerShell Lists)
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README

        [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)

# Scene Text Localization & Recognition Resources

A curated list of resources dedicated to scene text localization and recognition. Any suggestions and pull requests are welcome.

## Papers & Code

### Overview

- [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)

### Visual Geometry Group, University of Oxford

- [2016-IJCV, [M. Jaderberg](http://www.maxjaderberg.com)] Reading Text in the Wild with Convolutional Neural Networks  [`paper`](http://arxiv.org/abs/1412.1842) [`demo`](http://zeus.robots.ox.ac.uk/textsearch/#/search/)  [`homepage`](http://www.robots.ox.ac.uk/~vgg/research/text/)

- [2016-CVPR, [A Gupta](http://www.robots.ox.ac.uk/~ankush/)] Synthetic Data for Text Localisation in Natural Images [`paper`](http://www.robots.ox.ac.uk/~vgg/data/scenetext/gupta16.pdf) [`code`](https://github.com/ankush-me/SynthText) [`data`](http://www.robots.ox.ac.uk/~vgg/data/scenetext/)

- [2015-ICLR, [M. Jaderberg](http://www.maxjaderberg.com)] Deep structured output learning for unconstrained text recognition [`paper`](http://arxiv.org/abs/1412.5903)

- [2015-D.Phil Thesis, [M. Jaderberg](http://www.maxjaderberg.com)] Deep Learning for Text Spotting

 [`paper`](http://www.robots.ox.ac.uk/~vgg/publications/2015/Jaderberg15b/jaderberg15b.pdf)

- [2014-ECCV, [M. Jaderberg](http://www.maxjaderberg.com)] Deep Features for Text Spotting [`paper`](http://www.robots.ox.ac.uk/~vgg/publications/2014/Jaderberg14/jaderberg14.pdf) [`code`](https://bitbucket.org/jaderberg/eccv2014_textspotting) [`model`](https://bitbucket.org/jaderberg/eccv2014_textspotting) [`GitXiv`](http://gitxiv.com/posts/uB4y7QdD5XquEJ69c/deep-features-for-text-spotting)

- [2014-NIPS, [M. Jaderberg](http://www.maxjaderberg.com)] Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition [`paper`](http://www.robots.ox.ac.uk/~vgg/publications/2014/Jaderberg14c/jaderberg14c.pdf)  [`homepage`](http://www.robots.ox.ac.uk/~vgg/publications/2014/Jaderberg14c/) [`model`](http://www.robots.ox.ac.uk/~vgg/research/text/model_release.tar.gz)

### CUHK & SIAT

- [2016-arXiv] Accurate Text Localization in Natural Image with Cascaded Convolutional Text Network

 [`paper`](http://arxiv.org/abs/1603.09423)

- [2016-AAAI] Reading Scene Text in Deep Convolutional Sequences [`paper`](http://whuang.org/papers/phe2016_aaai.pdf)

- [2016-TIP] Text-Attentional Convolutional Neural Networks for Scene Text Detection [`paper`](http://whuang.org/papers/the2016_tip.pdf)

- [2014-ECCV] Robust Scene Text Detection with Convolution Neural Network Induced MSER Trees [`paper`](http://www.whuang.org/papers/whuang2014_eccv.pdf)

### Media and Communication Lab, HUST

- [2016-CVPR] Robust scene text recognition with automatic rectification [`paper`](http://arxiv.org/pdf/1603.03915v2.pdf)

- [2016-CVPR] Multi-oriented text detection with fully convolutional networks    [`paper`](http://mclab.eic.hust.edu.cn/UpLoadFiles/Papers/TextDectionFCN_CVPR16.pdf)

- [2015-CoRR] An End-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) [`code`](http://mclab.eic.hust.edu.cn/~xbai/CRNN/crnn_code.zip) [`github`](https://github.com/bgshih/crnn)

### AI Lab, Stanford

- [2012-ICPR, [Wang](http://cs.stanford.edu/people/twangcat/)] End-to-End Text Recognition with Convolutional Neural Networks [`paper`](http://www.cs.stanford.edu/~acoates/papers/wangwucoatesng_icpr2012.pdf) [`code`](http://cs.stanford.edu/people/twangcat/ICPR2012_code/SceneTextCNN_demo.tar) [`SVHN Dataset`](http://ufldl.stanford.edu/housenumbers/)

- [2012-PhD thesis, [David Wu](https://crypto.stanford.edu/people/dwu4/)] End-to-End Text Recognition with Convolutional Neural Networks [`paper`](http://cs.stanford.edu/people/dwu4/HonorThesis.pdf)

### Others

- [2018-CVPR] FOTS: Fast Oriented Text Spotting With a Unified Network [`paper`](http://openaccess.thecvf.com/content_cvpr_2018/html/Liu_FOTS_Fast_Oriented_CVPR_2018_paper.html)

- [2018-IJCAI] IncepText: A New Inception-Text Module with Deformable PSROI Pooling for Multi-Oriented Scene Text Detection [`paper`](https://arxiv.org/abs/1805.01167)

- [2018-AAAI] PixelLink: Detecting Scene Text via Instance Segmentation [`paper`](https://arxiv.org/abs/1801.01315) [`code`](https://github.com/ZJULearning/pixel_link)

- [2018-AAAI] SEE: Towards Semi-Supervised End-to-End Scene Text Recognition [`paper`](http://arxiv.org/abs/1712.05404)

[`code`](https://github.com/Bartzi/see)

- [2017-arXiv] Fused Text Segmentation Networks for Multi-oriented Scene Text Detection [`paper`](https://arxiv.org/pdf/1709.03272.pdf) 

- [2017-arXiv] WeText: Scene Text Detection under Weak Supervision [`paper`](https://arxiv.org/abs/1710.04826)

- [2017-ICCV] Single Shot Text Detector 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) [`code`](https://github.com/bgshih/seglink)

- [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)

- [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)

- [2016-CVPR] Recursive Recurrent Nets with Attention Modeling for OCR in the Wild [`paper`](http://arxiv.org/pdf/1603.03101v1.pdf)

- [2016-arXiv] COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural Images [`paper`](http://vision.cornell.edu/se3/wp-content/uploads/2016/01/1601.07140v1.pdf)

- [2016-arXiv] DeepText:A Unified Framework for Text Proposal Generation and Text Detection in Natural Images [`paper`](http://arxiv.org/abs/1605.07314)

- [2015 ICDAR] Object Proposals for Text Extraction in the Wild [`paper`](http://arxiv.org/abs/1509.02317) [`code`](https://github.com/lluisgomez/TextProposals)

- [2014-TPAMI] Word Spotting and Recognition with Embedded Attributes	 [`paper`](http://www.cvc.uab.es/~afornes/publi/journals/2014_PAMI_Almazan.pdf) [`homepage`](http://www.cvc.uab.es/~almazan/index/projects/words-att/index.html) [`code`](https://github.com/almazan/watts)

## Datasets

- [`MLT 2017`](http://rrc.cvc.uab.es/?ch=8&com=introduction) `2017`

  - 7200 training, 1800 validation images

  - Bounding box, text transcription, and script annotations

  - Task: text detection, script identification

- [`COCO-Text (Computer Vision Group, Cornell)`](http://vision.cornell.edu/se3/coco-text/)   `2016`

  - 63,686 images, 173,589 text instances, 3 fine-grained text attributes.

  - Task: text location and recognition

  - [`COCO-Text API`](https://github.com/andreasveit/coco-text)

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

  - 9 million 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`](http://cvit.iiit.ac.in/projects/SceneTextUnderstanding/IIIT5K.html)   `2012`

  - 5000 images from Scene Texts and born-digital (2k training and 3k testing images)

  - Each image 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)`](http://cs.stanford.edu/people/twangcat/#research)   `2012`

  - 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)`](http://www.iapr-tc11.org/mediawiki/index.php/MSRA_Text_Detection_500_Database_(MSRA-TD500))   `2012`

  - 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)`](http://tc11.cvc.uab.es/datasets/SVT_1)   `2010`

  - 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`](http://www.iapr-tc11.org/mediawiki/index.php/KAIST_Scene_Text_Database)   `2010`

  - 3000 images of indoor and outdoor scenes containing text

  - Korean, English (Number), and Mixed (Korean + English + Number)

  - Task: text location, segmantation and recognition

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

  - Over 74K images from natural images, as well as a set of synthetically generated characters 

  - Small single-character images of 62 characters (0-9, a-z, A-Z)

  - Task: text recognition

- `ICDAR Benchmark Datasets`

|Dataset| Discription | Competition Paper |

|---|---|----

|[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 11](https://medium.com/@khurram.pak522/scene-text-recognition-in-ios-11-2d0df8412151)[`github`](https://github.com/khurram18/SceneTextRecognitioniOS)