OCR文字，车牌，验证码识别 专知荟萃

入门学习

1. 端到端的OCR：基于CNN的实现
   • blog: [http://blog.xlvector.net/2016-05/mxnet-ocr-cnn/]
2. 如何用卷积神经网络CNN识别手写数字集？
   • blog: [http://www.cnblogs.com/charlotte77/p/5671136.html]
3. OCR文字识别用的是什么算法？
   • [https://www.zhihu.com/question/20191727]
4. 基于计算机视觉/深度学习打造先进OCR工作流 Creating a Modern OCR Pipeline Using Computer Vision and Deep Learning
   • [https://blogs.dropbox.com/tech/2017/04/creating-a-modern-ocr-pipeline-using-computer-vision-and-deep-learning/]
5. 车牌识别中的不分割字符的端到端(End-to-End)识别
   • [http://m.blog.csdn.net/Relocy/article/details/52174198]
6. 端到端的OCR：基于CNN的实现
   • [http://blog.xlvector.net/2016-05/mxnet-ocr-cnn/]
7. 腾讯OCR—自动识别技术，探寻文字真实的容颜
   • [http://blog.xlvector.net/2016-05/mxnet-ocr-cnn/]
8. Tesseract-OCR引擎 入门
   • [http://blog.csdn.net/xiaochunyong/article/details/7193744]
9. 汽车挡风玻璃VIN码识别
   • [https://github.com/DoctorDYL/VINOCR]
10. 车牌识别算法的关键技术及其研究现状
    • [http://www.siat.cas.cn/xscbw/xsqk/201012/W020101222564768411838.pdf]
11. 端到端的OCR：验证码识别
    • [https://zhuanlan.zhihu.com/p/21344595?refer=xlvector]

论文及代码

文字识别

1. Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks
   • intro: Google. Ian J. Goodfellow
   • arxiv: [https://arxiv.org/abs/1312.6082]
2. End-to-End Text Recognition with Convolutional Neural Networks
   • paper: [http://www.cs.stanford.edu/~acoates/papers/wangwucoatesng_icpr2012.pdf]
   • PhD thesis: [http://cs.stanford.edu/people/dwu4/HonorThesis.pdf]
3. Word Spotting and Recognition with Embedded Attributes
   • paper: [http://ieeexplore.ieee.org.sci-hub.org/xpl/articleDetails.jsp?arnumber=6857995&filter%3DAND%28p_IS_Number%3A6940341%29]
4. Reading Text in the Wild with Convolutional Neural Networks
   • arxiv: [http://arxiv.org/abs/1412.1842]
   • homepage: [http://www.robots.ox.ac.uk/~vgg/publications/2016/Jaderberg16/]
   • demo: [http://zeus.robots.ox.ac.uk/textsearch/#/search/]
   • code: [http://www.robots.ox.ac.uk/~vgg/research/text/]
5. Deep structured output learning for unconstrained text recognition
   • arxiv: [http://arxiv.org/abs/1412.5903]
6. Deep Features for Text Spotting
   • paper: [http://www.robots.ox.ac.uk/~vgg/publications/2014/Jaderberg14/jaderberg14.pdf]
   • bitbucket: [https://bitbucket.org/jaderberg/eccv2014_textspotting]
   • gitxiv: [http://gitxiv.com/posts/uB4y7QdD5XquEJ69c/deep-features-for-text-spotting]
7. Reading Scene Text in Deep Convolutional Sequences
   • arxiv: [http://arxiv.org/abs/1506.04395]
8. DeepFont: Identify Your Font from An Image
   • arxiv: [http://arxiv.org/abs/1507.03196]
9. An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition
   • intro: Convolutional Recurrent Neural Network
   • arxiv: [http://arxiv.org/abs/1507.05717]
   • github: [https://github.com/bgshih/crnn]
   • github: [https://github.com/meijieru/crnn.pytorch]
10. Recursive Recurrent Nets with Attention Modeling for OCR in the Wild
    • arxiv: [http://arxiv.org/abs/1603.03101]
11. Writer-independent Feature Learning for Offline Signature Verification using Deep Convolutional Neural Networks
    • arxiv: [http://arxiv.org/abs/1604.00974]
12. DeepText: A Unified Framework for Text Proposal Generation and Text Detection in Natural Images
    • arxiv: [http://arxiv.org/abs/1605.07314]
13. End-to-End Interpretation of the French Street Name Signs Dataset
    • paper: [http://link.springer.com/chapter/10.1007%2F978-3-319-46604-0_30]
    • github: [https://github.com/tensorflow/models/tree/master/street]
14. End-to-End Subtitle Detection and Recognition for Videos in East Asian Languages via CNN Ensemble with Near-Human-Level Performance
    • arxiv: [https://arxiv.org/abs/1611.06159]
15. Smart Library: Identifying Books in a Library using Richly Supervised Deep Scene Text Reading
    • arxiv: [https://arxiv.org/abs/1611.07385]
16. Improving Text Proposals for Scene Images with Fully Convolutional Networks
    • intro: Universitat Autonoma de Barcelona & University of Florence
    • intro: International Conference on Pattern Recognition - DLPR workshop
    • arxiv: [https://arxiv.org/abs/1702.05089]
17. Scene Text Eraser
    • [https://arxiv.org/abs/1705.02772]
18. Attention-based Extraction of Structured Information from Street View Imagery
    • intro: University College London & Google Inc
    • arxiv: [https://arxiv.org/abs/1704.03549]
    • github: [https://github.com/tensorflow/models/tree/master/attention_ocr]
19. STN-OCR: A single Neural Network for Text Detection and Text Recognition
    • arxiv: [https://arxiv.org/abs/1707.08831]
    • github: [https://github.com/Bartzi/stn-ocr]
20. Sequence to sequence learning for unconstrained scene text recognition
    • intro: master thesis
    • arxiv: [http://arxiv.org/abs/1607.06125]
21. Drawing and Recognizing Chinese Characters with Recurrent Neural Network
    • arxiv: [https://arxiv.org/abs/1606.06539]
22. Learning Spatial-Semantic Context with Fully Convolutional Recurrent Network for Online Handwritten Chinese Text Recognition
    • intro: correct rates: Dataset-CASIA 97.10% and Dataset-ICDAR 97.15%
    • arxiv: [https://arxiv.org/abs/1610.02616]
23. Stroke Sequence-Dependent Deep Convolutional Neural Network for Online Handwritten Chinese Character Recognition
    • arxiv: [https://arxiv.org/abs/1610.04057]
24. Visual attention models for scene text recognition
    • [https://arxiv.org/abs/1706.01487]
25. Focusing Attention: Towards Accurate Text Recognition in Natural Images
    • intro: ICCV 2017
    • arxiv: [https://arxiv.org/abs/1709.02054]
26. Scene Text Recognition with Sliding Convolutional Character Models
    • [https://arxiv.org/abs/1709.01727]
27. AdaDNNs: Adaptive Ensemble of Deep Neural Networks for Scene Text Recognition
    • [https://arxiv.org/abs/1710.03425]
28. A New Hybrid-parameter Recurrent Neural Networks for Online Handwritten Chinese Character Recognition
    • [https://arxiv.org/abs/1711.02809]
29. Arbitrarily-Oriented Text Recognition
    • intro: A method used in ICDAR 2017 word recognition competitions
    • arxiv: [https://arxiv.org/abs/1711.04226]

文字检测

1. Object Proposals for Text Extraction in the Wild

   • intro: ICDAR 2015
   • arxiv: [http://arxiv.org/abs/1509.02317]
   • github: [https://github.com/lluisgomez/TextProposals]

2. Text-Attentional Convolutional Neural Networks for Scene Text Detection

   • arxiv: [http://arxiv.org/abs/1510.03283]

3. Accurate Text Localization in Natural Image with Cascaded Convolutional Text Network

   • arxiv: [http://arxiv.org/abs/1603.09423]

4. Synthetic Data for Text Localisation in Natural Images

   • intro: CVPR 2016
   • project page: [http://www.robots.ox.ac.uk/~vgg/data/scenetext/]
   • arxiv: [http://arxiv.org/abs/1604.06646]
   • paper: [http://www.robots.ox.ac.uk/~vgg/data/scenetext/gupta16.pdf]
   • github: [https://github.com/ankush-me/SynthText]

5. Scene Text Detection via Holistic, Multi-Channel Prediction

   • arxiv: [http://arxiv.org/abs/1606.09002]

6. Detecting Text in Natural Image with Connectionist Text Proposal Network

   • intro: ECCV 2016
   • arxiv: [http://arxiv.org/abs/1609.03605]
   • github: [https://github.com/tianzhi0549/CTPN]
   • github: [https://github.com/qingswu/CTPN]
   • demo: [http://textdet.com/]
   • github: [https://github.com/eragonruan/text-detection-ctpn]

7. TextBoxes: A Fast Text Detector with a Single Deep Neural Network

   • intro: AAAI 2017
   • arxiv: [https://arxiv.org/abs/1611.06779]
   • github: [https://github.com/MhLiao/TextBoxes]
   • github: [https://github.com/xiaodiu2010/TextBoxes-TensorFlow]

8. Deep Matching Prior Network: Toward Tighter Multi-oriented Text Detection

   • intro: CVPR 2017
   • intro: F-measure 70.64%, outperforming the existing state-of-the-art method with F-measure 63.76%
   • arxiv: [https://arxiv.org/abs/1703.01425]

9. Detecting Oriented Text in Natural Images by Linking Segments

   • intro: CVPR 2017
   • arxiv: [https://arxiv.org/abs/1703.06520]
   • github: [https://github.com/dengdan/seglink]

10. Deep Direct Regression for Multi-Oriented Scene Text Detection

    • arxiv: [https://arxiv.org/abs/1703.08289]

11. Cascaded Segmentation-Detection Networks for Word-Level Text Spotting

    • [https://arxiv.org/abs/1704.00834]

12. WordFence: Text Detection in Natural Images with Border Awareness

    • intro: ICIP 2017
    • arcxiv: [https://arxiv.org/abs/1705.05483]

13. SSD-text detection: Text Detector

    • intro: A modified SSD model for text detection
    • github: [https://github.com/oyxhust/ssd-text_detection]

14. R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection

    • intro: Samsung R&D Institute China
    • arxiv: [https://arxiv.org/abs/1706.09579]

15. R-PHOC: Segmentation-Free Word Spotting using CNN

    • intro: ICDAR 2017
    • arxiv: [https://arxiv.org/abs/1707.01294]

16. Towards End-to-end Text Spotting with Convolutional Recurrent Neural Networks

    • [https://arxiv.org/abs/1707.03985]

17. EAST: An Efficient and Accurate Scene Text Detector

    • intro: CVPR 2017
    • arxiv: [https://arxiv.org/abs/1704.03155]
    • github: [https://github.com/argman/EAST]

18. Deep Scene Text Detection with Connected Component Proposals

    • intro: Amap Vision Lab, Alibaba Group
    • arxiv: [https://arxiv.org/abs/1708.05133]

19. Single Shot Text Detector with Regional Attention

    • intro: ICCV 2017
    • arxiv: [https://arxiv.org/abs/1709.00138]
    • github: [https://github.com/BestSonny/SSTD]
    • code: [http://sstd.whuang.org]

20. Fused Text Segmentation Networks for Multi-oriented Scene Text Detection

    • [https://arxiv.org/abs/1709.03272]

21. Deep Residual Text Detection Network for Scene Text

    • intro: IAPR International Conference on Document Analysis and Recognition 2017. Samsung R&D Institute of China, Beijing
    • arxiv: [https://arxiv.org/abs/1711.04147]

22. Feature Enhancement Network: A Refined Scene Text Detector

    • intro: AAAI 2018
    • arxiv: [https://arxiv.org/abs/1711.04249]

23. ArbiText: Arbitrary-Oriented Text Detection in Unconstrained Scene

    • [https://arxiv.org/abs/1711.11249]

验证码破解

1. Using deep learning to break a Captcha system
   • intro: "Using Torch code to break simplecaptcha with 92% accuracy"
   • blog: [https://deepmlblog.wordpress.com/2016/01/03/how-to-break-a-captcha-system/]
   • github: [https://github.com/arunpatala/captcha]
2. Breaking reddit captcha with 96% accuracy
   • blog: [https://deepmlblog.wordpress.com/2016/01/05/breaking-reddit-captcha-with-96-accuracy/]
   • github: [https://github.com/arunpatala/reddit.captcha]
3. I’m not a human: Breaking the Google reCAPTCHA
   • paper: [https://www.blackhat.com/docs/asia-16/materials/asia-16-Sivakorn-Im-Not-a-Human-Breaking-the-Google-reCAPTCHA-wp.pdf]
4. Neural Net CAPTCHA Cracker
   • slides: [http://www.cs.sjsu.edu/faculty/pollett/masters/Semesters/Spring15/geetika/CS298%20Slides%20-%20PDF]
   • github: [https://github.com/bgeetika/Captcha-Decoder]
   • demo: [http://cp-training.appspot.com/]
5. Recurrent neural networks for decoding CAPTCHAS
   • blog: [https://deepmlblog.wordpress.com/2016/01/12/recurrent-neural-networks-for-decoding-captchas/]
   • demo: [http://simplecaptcha.sourceforge.net/]
   • code: [http://sourceforge.net/projects/simplecaptcha/]
6. Reading irctc captchas with 95% accuracy using deep learning
   • github: [https://github.com/arunpatala/captcha.irctc]
7. I Am Robot: Learning to Break Semantic Image CAPTCHAs
   • intro: automatically solving 70.78% of the image reCaptchachallenges, while requiring only 19 seconds per challenge. apply to the Facebook image captcha and achieve an accuracy of 83.5%
   • paper: [http://www.cs.columbia.edu/~polakis/papers/sivakorn_eurosp16.pdf]
8. SimGAN-Captcha
   • intro: Solve captcha without manually labeling a training set
   • github: [https://github.com/rickyhan/SimGAN-Captcha]

手写体识别

1. High Performance Offline Handwritten Chinese Character Recognition Using GoogLeNet and Directional Feature Maps
   • arxiv: [http://arxiv.org/abs/1505.04925]
   • github: [https://github.com/zhongzhuoyao/HCCR-GoogLeNet]
2. Recognize your handwritten numbers
   • [https://medium.com/@o.kroeger/recognize-your-handwritten-numbers-3f007cbe46ff#.jllz62xgu]
3. Handwritten Digit Recognition using Convolutional Neural Networks in Python with Keras
   • blog: [http://machinelearningmastery.com/handwritten-digit-recognition-using-convolutional-neural-networks-python-keras/]
4. MNIST Handwritten Digit Classifier
   • github: [https://github.com/karandesai-96/digit-classifier]
5. LeNet – Convolutional Neural Network in Python
   • blog: [http://www.pyimagesearch.com/2016/08/01/lenet-convolutional-neural-network-in-python/]
6. Scan, Attend and Read: End-to-End Handwritten Paragraph Recognition with MDLSTM Attention
   • arxiv: [http://arxiv.org/abs/1604.03286]
7. MLPaint: the Real-Time Handwritten Digit Recognizer
   • blog: [http://blog.mldb.ai/blog/posts/2016/09/mlpaint/]
   • github: [https://github.com/mldbai/mlpaint]
   • demo: [https://docs.mldb.ai/ipy/notebooks/_demos/_latest/Image%20Processing%20with%20Convolutions.html]
8. Training a Computer to Recognize Your Handwriting
   • [https://medium.com/@annalyzin/training-a-computer-to-recognize-your-handwriting-24b808fb584#.gd4pb9jk2]
9. Using TensorFlow to create your own handwriting recognition engine
   • blog: [https://niektemme.com/2016/02/21/tensorflow-handwriting/]
   • github: [https://github.com/niektemme/tensorflow-mnist-predict/]
10. Building a Deep Handwritten Digits Classifier using Microsoft Cognitive Toolkit
    • blog: [https://medium.com/@tuzzer/building-a-deep-handwritten-digits-classifier-using-microsoft-cognitive-toolkit-6ae966caec69#.c3h6o7oxf]
    • github: [https://github.com/tuzzer/ai-gym/blob/a97936619cf56b5ed43329c6fa13f7e26b1d46b8/MNIST/minist_softmax_cntk.py]
11. Hand Writing Recognition Using Convolutional Neural Networks
    • intro: This CNN-based model for recognition of hand written digits attains a validation accuracy of 99.2% after training for 12 epochs. Its trained on the MNIST dataset on Kaggle.
    • github: [https://github.com/ayushoriginal/HandWritingRecognition-CNN]
12. Design of a Very Compact CNN Classifier for Online Handwritten Chinese Character Recognition Using DropWeight and Global Pooling
    • intro: 0.57 MB, performance is decreased only by 0.91%.
    • arxiv: [https://arxiv.org/abs/1705.05207]
13. Handwritten digit string recognition by combination of residual network and RNN-CTC
    • [https://arxiv.org/abs/1710.03112]

车牌识别

1. Reading Car License Plates Using Deep Convolutional Neural Networks and LSTMs
   • arxiv: [http://arxiv.org/abs/1601.05610]
2. Number plate recognition with Tensorflow
   • blog: [http://matthewearl.github.io/2016/05/06/cnn-anpr/]
   • github: [https://github.com/matthewearl/deep-anpr]
3. end-to-end-for-plate-recognition
   • github: [https://github.com/szad670401/end-to-end-for-chinese-plate-recognition]
4. Segmentation-free Vehicle License Plate Recognition using ConvNet-RNN
   • intro: International Workshop on Advanced Image Technology, January, 8-10, 2017. Penang, Malaysia. Proceeding IWAIT2017
   • arxiv: [https://arxiv.org/abs/1701.06439]
5. License Plate Detection and Recognition Using Deeply Learned Convolutional Neural Networks
   • arxiv: [https://arxiv.org/abs/1703.07330]
   • api: [https://www.sighthound.com/products/cloud]
6. Adversarial Generation of Training Examples for Vehicle License Plate Recognition
   • [https://arxiv.org/abs/1707.03124]
7. Towards End-to-End Car License Plates Detection and Recognition with Deep Neural Networks
   • [https://arxiv.org/abs/1709.08828]

实战项目

1. 多标签分类,端到端基于mxnet的中文车牌识别
   • [https://github.com/szad670401/end-to-end-for-chinese-plate-recognition]
2. 中国二代身份证光学识别
   • [https://github.com/KevinGong2013/ChineseIDCardOCR]
3. EasyPR 一个开源的中文车牌识别系统
   • [https://github.com/liuruoze/EasyPR]
4. 汽车挡风玻璃VIN码识别
   • [https://github.com/DoctorDYL/VINOCR]
5. CLSTM : A small C++ implementation of LSTM networks, focused on OCR
   • github: [https://github.com/tmbdev/clstm]
6. OCR text recognition using tensorflow with attention
   • github: [https://github.com/pannous/caffe-ocr]
   • github: [https://github.com/pannous/tensorflow-ocr]
7. Digit Recognition via CNN: digital meter numbers detection
   • github: [https://github.com/SHUCV/digit]
8. Attention-OCR: Visual Attention based OCR
   • github: [https://github.com/da03/Attention-OCR]
9. umaru: An OCR-system based on torch using the technique of LSTM/GRU-RNN, CTC and referred to the works of rnnlib and clstm
   • github: [https://github.com/edward-zhu/umaru]
10. Tesseract.js: Pure Javascript OCR for 62 Languages
    • homepage: [http://tesseract.projectnaptha.com/]
    • github: [https://github.com/naptha/tesseract.js]
11. DeepHCCR: Offline Handwritten Chinese Character Recognition based on GoogLeNet and AlexNet
    • github: [https://github.com/chongyangtao/DeepHCCR]
12. deep ocr: make a better chinese character recognition OCR than tesseract
    • [https://github.com/JinpengLI/deep_ocr]
13. Practical Deep OCR for scene text using CTPN + CRNN
    • [https://github.com/AKSHAYUBHAT/DeepVideoAnalytics/blob/master/notebooks/OCR/readme.md]
14. Text-Detection-using-py-faster-rcnn-framework
    • github: [https://github.com/jugg1024/Text-Detection-with-FRCN]
15. ocropy: Python-based tools for document analysis and OCR
    • github: [https://github.com/tmbdev/ocropy]
16. Extracting text from an image using Ocropus
    • blog: [http://www.danvk.org/2015/01/09/extracting-text-from-an-image-using-ocropus.html]

视频

1. LSTMs for OCR

• youtube: [https://www.youtube.com/watch?v=5vW8faXvnrc]

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