• [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] SceneText Detection via Holistic, Multi-Channel Prediction [paper]

• [2017-arvix 文档识别] Full-Page TextRecognition : Learning Where to Start and When to Stop[paper]

• [2016-AAAI]Reading Scene Text in Deep Convolutional Sequences [paper]

• [2015-ICDAR]Automatic Script Identification in the Wild[paper]

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 [code]

• CLSTM : A small C++ implementation of LSTM networks,focused on OCR [code]

• Convolutional Recurrent Neural Network,Torch7 based [code]

• Attention-OCR: Visual Attention based OCR [code]

• Umaru: An OCR-system based on torch using the technique of LSTM/GRU-RNN, CTC and referred to the works of rnnlib and clstm [code]

• AKSHAYUBHAT/DeepVideoAnalytics (CTPN+CRNN) code
• ankush-me/SynthText code
• JarveeLee/SynthText_Chinese_version code

• DeepFont:Identify Your Font from An Image [Paper]

• Writer-independent Feature Learning for Offline Signature Verification using Deep Convolutional Neural Networks [Paper]

• End-to-End Interpretation of the French Street Name Signs Dataset [paper] [code]

• Extracting text from an image using Ocropus [blog]

• [2016-arXiv]Drawingand Recognizing Chinese Characters with Recurrent Neural Network [paper]

• Learning Spatial-Semantic Context with Fully Convolutional Recurrent Network for Online Handwritten Chinese Text Recognition [paper]

• Stroke Sequence-Dependent Deep Convolutional Neural Network for Online Handwritten Chinese Character Recognition [paper]

• High Performance Offline Handwritten Chinese Character Recognition Using GoogLeNet and Directional Feature Maps [paper]
• DeepHCCR:Offline Handwritten Chinese Character Recognition based on GoogLeNet and AlexNet (With CaffeModel) [code]

• 如何用卷积神经网络CNN识别手写数字集？[blog][blog1][blog2] [blog4] [blog5] [code6]

• Scan,Attend and Read: End-to-End Handwritten Paragraph Recognition with MDLSTMAttention [paper]

• MLPaint:the Real-Time Handwritten Digit Recognizer [blog][code][demo]

• caffe-ocr: OCR with caffe deep learning framework [code] (单字分类器)

• ReadingCar License Plates Using Deep Convolutional Neural Networks and LSTMs  [paper]

• Numberplate recognition with Tensorflow [blog]
• end-to-end-for-plate-recognition[code]

• ApplyingOCR Technology for Receipt Recognition[blog][mirror]

• [2017-Arvix]Using Synthetic Data to Train NeuralNetworks is Model-Based Reasoning[paper]
  

• Using deep learning to break a Captcha system [blog]

• Breakingreddit captcha with 96% accuracy [blog]

• I'm not a human: Breaking the Google reCAPTCHA (https://www.blackhat.com/docs/asia-16/materials/asia-16-Sivakorn-Im-Not-a-Human-Breaking-the-Google-reCAPTCHA-wp.pdf)

• NeuralNet CAPTCHA Cracker [slides] [code] [demo]

• Recurrentneural networks for decoding CAPTCHAS [blog] [code] [demo]

• Readingirctc captchas with 95% accuracy using deep learning [code]

• End-to-EndOCR：based on CNN [blog]

• IAm Robot: (Deep) Learning to Break Semantic Image CAPTCHAs [paper]

• http://handong1587.github.io/deep_learning/2015/10/09/ocr.html
• https://github.com/chongyangtao/Awesome-Scene-Text-Recognition