作者：tangzhenyu

編譯：ronghuaiyang

導讀

OCR相關的資源，綜述、論文、代碼、數據集、博客、在線服務等等，應有盡有，作者整理的非常的全面。

項目地址：

https://github.com/tangzhenyu/Scene-Text-Understanding

場景文字理解

綜述

• [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`

場景文字檢測

• [2018-CPVR] Multi-Oriented Scene Text Detection via Corner Localization and Region Segmentation [Paper]
• [2018-arxiv] PixelLink: Detecting Scene Text via Instance Segmentation [Paper]
• [2018-AAAI] SEE: Towards Semi-Supervised End-to-End Scene Text Recognition [Paper]
• [2018-arxiv] TextBoxes++: A Single-Shot Oriented Scene Text Detector[Paper]
• [2017-arxiv] Attention-based Extraction of Structured [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_textspottinghttps://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.htmlhttps://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]

場景文字識別

• [2017-ICCV] WeText: Scene Text Detection under Weak Supervision [Paper]
• [2017-ICCV] Single Shot Text Detector with Regional Attention [Paper] [Code]
• [2017-ICCV] Self-organized Text Detection with Minimal Post-processing via Border Learning [Paper]
• [2017-ICCV] Focusing Attention: Towards Accurate Text Recognition in Natural Images [Paper]
• [2017-ICCV] Towards End-to-end Text Spotting with Convolutional Recurrent Neural Networks [Paper]
• [2017-CVPR] Unambiguous Text Localization and Retrieval for Cluttered Scenes [Paper]
• [2017-ICCV] WordSup: Exploiting Word Annotations for Character based Text Detection [Paper]
• [2017-ICCV] Deep TextSpotter: An End-to-End Trainable Scene Text Localization and Recognition Framework [Paper][Code]
• [2017-arXiv] Cascaded Segmentation-Detection Networks for Word-Level Text Spotting [Paper]
• [2017-AAAI] Detection and Recognition of Text Embedding in Online Images via Neural Context Models [Paper] [Code]
• [2017-arXiv] Improving Text Proposal for Scene Images with Fully Convolutional Networks [Paper]
• [2017-AAAI] TextBoxes: A Fast TextDetector with a Single Deep Neural Network [Paper] [Code] `github 代碼`
• [2017-CVPR] Detecting Oriented Text in Natural Images by Linking Segments [Paper]
• [2017-arXiv] Arbitrary-Oriented Scene Text Detection via Rotation Proposals [Paper]
• [2017-CVPR] Deep Matching Prior Network: Toward Tighter Multi-oriented Text Detection [Paper]
• [2016-arXiv] DeepText:A Unified Framework for Text Proposal Generation and Text Detection in Natural Images [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/

博士論文

• [2016-PhD Thesis] Context Modeling for Semantic Text Matching and Scene Text Detection [Paper]
• [2015-PhD Thesis] Deep Learning for Text Spotting [Paper]
• [2012-PhD thesis] End-to-End Text Recognition with Convolutional Neural Networks [Paper]

文字檢測

• [2018-arxiv] TextBoxes++: A Single-Shot Oriented Scene Text Detector [Paper]

數據集

PowerPoint Text Detection and Recognition Dataset 2017

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

博客

• 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]

線上服務

開原始碼

• 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

手寫識別

• [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 Recognitionhttps://arxiv.org/abs/1610.04057
• High Performance Offline Handwritten Chinese Character Recognition Using GoogLeNet and Directional Feature Mapshttp://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 MDLSTMAttentionhttp://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

證照識別

• 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/

原文連結：https://github.com/tangzhenyu/Scene-Text-Understanding