【总结】文字检测与识别资源

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

[2017-CVPR]EAST: An Efficient and Accurate Scene Text Detector [paper]

[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]

 

[2016-arXiv]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-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 TextRecognition : 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

 

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

Smallsingle-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)

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
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

 

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

原文：http://blog.csdn.net/peaceinmind/article/details/51387367