本文写成时主要参考了[1,2], 后面加了一些自己收集的，不过大家都在更新，所以区别不是很大~
综述
[2015-PAMI-Overview]Text Detection and Recognition in Imagery: A Survey[paper]
[2014-Front.Comput.Sci-Overview]Scene Text Detection and Recognition: Recent Advances and Future Trends[paper]
自然场景文字检测
[2018-arxiv]TextBoxes++: ASingle-Shot Oriented Scene Text Detector[paper]
[2018-arxiv]FOTS: Fast OrientedText Spotting with a Unified Network[paper]
[2018-AAAI] PixelLink: DetectingScene Text via Instance Segmentation[paper]
[2017-arXiv]Fused Text Segmentation Networks for Multi-oriented Scene Text Detection[paper]
[2017-arXiv]WeText: Scene Text Detection under Weak Supervision[paper]
[2017-ICCV]Single Shot Text Detector with Regional Attention[pdf]
[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][code]
[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]
[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[paper]
[2015-CVPR]Symmetry-Based Text Line Detection in Natural Scenes[paper][code]
[2015-ICCV]FASText: Efficient unconstrained scene text detector[paper][code]
[2015-D.PhilThesis] Deep Learning for Text Spotting [paper]
[2015 ICDAR]Object Proposals for Text Extraction in the Wild [paper] [code]
[2014-ECCV] Deep Features for Text Spotting [paper] [code] [model] [GitXiv]
[2014-TPAMI] Word Spotting and Recognition with Embedded Attributes [paper] [homepage] [code]
[2014-TPRMI]Robust Text Detection in Natural Scene Images[paper]
[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][code]
[2010-CVPR]Detecting Text in Natural Scenes with Stroke Width Transform [paper] [code]
自然场景文字识别
[2017-arXiv]AdaDNNs: Adaptive Ensemble of Deep Neural Networks for Scene Text Recognition [paper]
[2017-arXiv]STN-OCR: A single Neural Network for Text Detection and Text Recognition[paper][code]
[2017-arXiv]Auto-Encoder Guided GAN for Chinese Calligraphy Synthesis[paper]
[2017-AAAI-网络图片]Detection and Recognition of Text Embedded in Online Images via Neural Context Models[paper][project]
[2017-arvix 文档识别] Full-Page Text Recognition : Learning Where to Start and When to Stop[paper]
[2016-AAAI]Reading Scene Text in Deep Convolutional Sequences [paper]
[2016-IJCV]Reading Text in the Wild with Convolutional Neural Networks [paper] [demo] [homepage]
[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] [code]
[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 [paperhomepage] [model]
[2014-TIP] A Unified Framework for Multi-Oriented Text Detection and Recognition [paper]
[2012-ICPR]End-to-End Text Recognition with Convolutional Neural Networks [paper] [code] [SVHN Dataset]
数据集
Total-Text 2017
1555 images,11459 text instances, includes curved text
COCO-Text (ComputerVision Group, Cornell) 2016
63,686images, 173,589 text instances, 3 fine-grained text attributes.
Task:text location and recognition
Synthetic Data for Text Localisation in Natural Image (VGG)2016
800k thousand images
8 million synthetic word instances
Synthetic Word Dataset (Oxford, VGG) 2014
9million images covering 90k English words
Task:text recognition, segmentation
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
StanfordSynth(Stanford, AI Group) 2012
Smallsingle-character images of 62 characters (0-9, a-z, A-Z)
Task:text recognition
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
350 high resolution images (average size 1260 × 860) (100 images for training and 250 images for testing)
Onlyword level bounding boxes are provided with case-insensitive labels
Task:text location
KAIST Scene_Text Database 2010
3000images of indoor and outdoor scenes containing text
Korean,English (Number), and Mixed (Korean + English + Number)
Task:text location, segmentation and recognition
Chars74k 2009
Over74K 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
ICDARBenchmark Datasets
Dataset | Discription | Competition Paper |
1000 training images and 500 testing images | paper | |
229 training images and 233 testing images | paper | |
229 training images and 255 testing images | paper | |
1001 training images and 489 testing images | paper | |
181 training images and 251 testing images(word level and character level) | paper |
开源库
Tesseract: c++ based tools for documents analysis and OCR,support 60+ languages [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]
其他
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]
手写字识别
[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] [github]
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)
牌照等识别
ReadingCar License Plates Using Deep Convolutional Neural Networks and LSTMs [paper]
Numberplate recognition with Tensorflow [blog] [code]
end-to-end-for-plate-recognition[code]
ApplyingOCR Technology for Receipt Recognition[blog][mirror]
破解验证码
[2017-Arvix]Using Synthetic Data to Train NeuralNetworks is Model-Based Reasoning[paper]
Using deep learning to break a Captcha system [blog] [code]
Breakingreddit captcha with 96% accuracy [blog] [code]
I'mnot a human: Breaking the Google reCAPTCHA [paper]
NeuralNet CAPTCHA Cracker [slides] [code] [demo]
Recurrentneural networks for decoding CAPTCHAS [blog] [code] [demo]
Readingirctc captchas with 95% accuracy using deep learning [code]
端到端的OCR：基于CNN的实现 [blog]
IAm Robot: (Deep) Learning to Break Semantic Image CAPTCHAs [paper]
参考
[1]http://handong1587.github.io/deep_learning/2015/10/09/ocr.html
[2]https://github.com/chongyangtao/Awesome-Scene-Text-Recognition