• [2015-PAMI] Text Detection and Recognition in Imagery: A Survey paper
• [2014-Front.Comput.Sci] Scene Text Detection and Recognition: Recent Advances and Future Trends paper

• [2017-ICCV]Single Shot TextDetector with Regional Attention [Paper]
• [2017-ICCV]WordSup: Exploiting Word Annotations for Character based Text Detection [Paper]
• [2017-arXiv]R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection[Paper]
• [2017-CVPR]EAST: An Efficient and Accurate Scene Text Detector [Paper] [Code]
• [2017-arXiv]Cascaded Segmentation-Detection Networks for Word-Level Text Spotting[Paper]
• [2017-arXiv]Deep Direct Regression for Multi-Oriented Scene Text Detection [Paper]
• [2017-CVPR]Detecting oriented text in natural images by linking segments [Paper]
• [2017-CVPR]Deep Matching Prior Network: Toward Tighter Multi-oriented Text Detection [Paper]
• [2017-arXiv]Arbitrary-Oriented Scene Text Detection via Rotation Proposals [Paper]
• [2017-AAAI]TextBoxes: A Fast Text Detector with a Single Deep Neural Network[Paper][Code]
• [2016-arXiv]Accurate Text Localization in Natural Image with Cascaded Convolutional TextNetwork [Paper]
• [2016-arXiv]DeepText : A Unified Framework for Text Proposal Generation and Text Detectionin Natural Images [Paper] [Data]
• [2017-PR]TextProposals: a Text-specific Selective Search Algorithm for Word Spotting in the Wild [paper] [code]
• [2016-arXiv] Scene Text Detection via Holistic, Multi-Channel Prediction [Paper]
• [2016-CVPR] CannyText Detector: Fast and Robust Scene Text Localization Algorithm [Paper]
• [2016-CVPR]Synthetic Data for Text Localisation in Natural Images[Paper] [Data] [Code]
• [2016-ECCV]Detecting Text in Natural Image with Connectionist Text Proposal Network[Paper] [Demo][Code]
• [2016-TIP]Text-Attentional Convolutional Neural Networks for Scene Text Detection[Paper]
• [2016-IJDAR]TextCatcher: a method to detect curved and challenging text in natural scenes[Paper]
• [2016-CVPR]Multi-oriented text detection with fully convolutional networks[Paper]
• [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://github.com/MichalBusta/FASText
• [2015-D.PhilThesis] Deep Learning for Text Spotting [Paper]
• [2015 ICDAR]Object Proposals for Text Extraction in the Wild [Paper] https://github.com/lluisgomez/TextProposals
• [2014-ECCV] Deep Features for Text Spotting [Paper] 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/~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]
• [2013-ICCV] Photo OCR: Reading Text in Uncontrolled Conditions [Paper]
• [2012-CVPR]Real-time scene text localization and recognition [Paper]
• [2010-CVPR]Detecting Text in Natural Scenes with Stroke Width Transform [Paper]

• [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]
• [2016-IJCV]Reading Text in the Wild with Convolutional Neural Networks [Paper] 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]
• [2016-CVPR] Robust Scene Text Recognition with Automatic Rectification [Paper]
• [2016-NIPs] Generative Shape Models: Joint Text Recognition and Segmentation with Very Little Training Data [Paper]
• [2015-CoRR] AnEnd-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition [Paper] https://github.com/bgshih/crnn
• [2015-ICDAR]Automatic Script Identification in the Wild [Paper]
• [2015-ICLR] Deep structured output learning for unconstrained text recognition [Paper]
• [2014-NIPS]Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition [Paper] 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://cs.stanford.edu/people/twangcat/ICPR2012_code/SceneTextCNN_demo.tar http://ufldl.stanford.edu/housenumbers/

COCO-Text (ComputerVision Group, Cornell) 2016

• 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

• 9million images covering 90k English words
• Task:text recognition, segmentation
• download

IIIT 5K-Words 2012

• 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

StanfordSynth(Stanford, AI Group) 2012

• Small single-character images of 62 characters (0-9, a-z, A-Z)
• Task:text recognition
• download

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) 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 2010

• 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

• 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 2015	1000 training images and 500 testing images	paper
ICDAR 2013	229 training images and 233 testing images	paper
ICDAR 2011	229 training images and 255 testing images	paper
ICDAR 2005	1001 training images and 489 testing images	paper
ICDAR 2003	181 training images and 251 testing images(word level and character level)	paper

• Scene Text Detection with OpenCV 3
• Handwritten numbers detection and recognition
• Applying OCR Technology for Receipt Recognition
• Convolutional Neural Networks for Object(Car License) Detection
• Extracting text from an image using Ocropus
• Number plate recognition with Tensorflow [github]
• Using deep learning to break a Captcha system report [github]
• Breaking reddit captcha with 96% accuracy [github]

• Tesseract c++ based tools for documents analysis and OCR [code]
• Ocropy: 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
• ankush-me/SynthText code
• JarveeLee/SynthText_Chinese_version code

• [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">

• 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

• 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/