利用计算机自动识别字符的技术,是模式识别应用的一个重要领域。人们在生产和生活中,要处理大量的文字、报表和文本。为了减轻人们的劳动,提高处理效率,50年代开始探讨一般文字识别方法,并研制出光学字符识别器。60年代出现了采用磁性墨水和特殊字体的实用机器。60年代后期,出现了多种字体和手写体文字识别机,其识别精度和机器性能都基本上能满足要求。如用于信函分拣的手写体数字识别机和印刷体英文数字识别机。70年代主要研究文字识别的基本理论和研制高性能的文字识别机,并着重于汉字识别的研究。

知识荟萃

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
  2. 如何用卷积神经网络CNN识别手写数字集?
  3. OCR文字识别用的是什么算法?
  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
  5. 车牌识别中的不分割字符的端到端(End-to-End)识别
  6. 端到端的OCR:基于CNN的实现
  7. 腾讯OCR—自动识别技术,探寻文字真实的容颜
  8. Tesseract-OCR引擎 入门
  9. 汽车挡风玻璃VIN码识别
  10. 车牌识别算法的关键技术及其研究现状
  11. 端到端的OCR:验证码识别

论文及代码

文字识别

  1. Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks
  2. End-to-End Text Recognition with Convolutional Neural Networks
  3. Word Spotting and Recognition with Embedded Attributes
  4. Reading Text in the Wild with Convolutional Neural Networks
  5. Deep structured output learning for unconstrained text recognition
  6. Deep Features for Text Spotting
  7. Reading Scene Text in Deep Convolutional Sequences
  8. DeepFont: Identify Your Font from An Image
  9. An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition
  10. Recursive Recurrent Nets with Attention Modeling for OCR in the Wild
  11. Writer-independent Feature Learning for Offline Signature Verification using Deep Convolutional Neural Networks
  12. DeepText: A Unified Framework for Text Proposal Generation and Text Detection in Natural Images
  13. End-to-End Interpretation of the French Street Name Signs Dataset
  14. End-to-End Subtitle Detection and Recognition for Videos in East Asian Languages via CNN Ensemble with Near-Human-Level Performance
  15. Smart Library: Identifying Books in a Library using Richly Supervised Deep Scene Text Reading
  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
  18. Attention-based Extraction of Structured Information from Street View Imagery
  19. STN-OCR: A single Neural Network for Text Detection and Text Recognition
  20. Sequence to sequence learning for unconstrained scene text recognition
  21. Drawing and Recognizing Chinese Characters with Recurrent Neural Network
  22. Learning Spatial-Semantic Context with Fully Convolutional Recurrent Network for Online Handwritten Chinese Text Recognition
  23. Stroke Sequence-Dependent Deep Convolutional Neural Network for Online Handwritten Chinese Character Recognition
  24. Visual attention models for scene text recognition
  25. Focusing Attention: Towards Accurate Text Recognition in Natural Images
  26. Scene Text Recognition with Sliding Convolutional Character Models
  27. AdaDNNs: Adaptive Ensemble of Deep Neural Networks for Scene Text Recognition
  28. A New Hybrid-parameter Recurrent Neural Networks for Online Handwritten Chinese Character Recognition
  29. Arbitrarily-Oriented Text Recognition

文字检测

  1. Object Proposals for Text Extraction in the Wild

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

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

  4. Synthetic Data for Text Localisation in Natural Images

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

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

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

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

  9. Detecting Oriented Text in Natural Images by Linking Segments

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

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

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

  13. SSD-text detection: Text Detector

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

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

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

  17. EAST: An Efficient and Accurate Scene Text Detector

  18. Deep Scene Text Detection with Connected Component Proposals

  19. Single Shot Text Detector with Regional Attention

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

  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

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

验证码破解

  1. Using deep learning to break a Captcha system
  2. Breaking reddit captcha with 96% accuracy
  3. I’m not a human: Breaking the Google reCAPTCHA
  4. Neural Net CAPTCHA Cracker
  5. Recurrent neural networks for decoding CAPTCHAS
  6. Reading irctc captchas with 95% accuracy using deep learning
  7. I Am Robot: Learning to Break Semantic Image CAPTCHAs
  8. SimGAN-Captcha

手写体识别

  1. High Performance Offline Handwritten Chinese Character Recognition Using GoogLeNet and Directional Feature Maps
  2. Recognize your handwritten numbers
  3. Handwritten Digit Recognition using Convolutional Neural Networks in Python with Keras
  4. MNIST Handwritten Digit Classifier
  5. LeNet – Convolutional Neural Network in Python
  6. Scan, Attend and Read: End-to-End Handwritten Paragraph Recognition with MDLSTM Attention
  7. MLPaint: the Real-Time Handwritten Digit Recognizer
  8. Training a Computer to Recognize Your Handwriting
  9. Using TensorFlow to create your own handwriting recognition engine
  10. Building a Deep Handwritten Digits Classifier using Microsoft Cognitive Toolkit
  11. Hand Writing Recognition Using Convolutional Neural Networks
  12. Design of a Very Compact CNN Classifier for Online Handwritten Chinese Character Recognition Using DropWeight and Global Pooling
  13. Handwritten digit string recognition by combination of residual network and RNN-CTC

车牌识别

  1. Reading Car License Plates Using Deep Convolutional Neural Networks and LSTMs
  2. Number plate recognition with Tensorflow
  3. end-to-end-for-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
  6. Adversarial Generation of Training Examples for Vehicle License Plate Recognition
  7. Towards End-to-End Car License Plates Detection and Recognition with Deep Neural Networks

实战项目

  1. 多标签分类,端到端基于mxnet的中文车牌识别
  2. 中国二代身份证光学识别
  3. EasyPR 一个开源的中文车牌识别系统
  4. 汽车挡风玻璃VIN码识别
  5. CLSTM : A small C++ implementation of LSTM networks, focused on OCR
  6. OCR text recognition using tensorflow with attention
  7. Digit Recognition via CNN: digital meter numbers detection
  8. Attention-OCR: Visual Attention based 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
  10. Tesseract.js: Pure Javascript OCR for 62 Languages
  11. DeepHCCR: Offline Handwritten Chinese Character Recognition based on GoogLeNet and AlexNet
  12. deep ocr: make a better chinese character recognition OCR than tesseract
  13. Practical Deep OCR for scene text using CTPN + CRNN
  14. Text-Detection-using-py-faster-rcnn-framework
  15. ocropy: Python-based tools for document analysis and OCR
  16. Extracting text from an image using Ocropus

视频

  1. LSTMs for OCR

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会议介绍:

11月2日,由极视角主办,英特尔、UCloud作为合作伙伴的 CV101计算机视觉青年开发者技术与应用大会 在深圳福田盛大开幕。本次大会聚焦于人工智能落地应用最广的计算机视觉领域,汇聚全球极具代表性的行业专家及一流企业家,设技术演讲报告与前沿算法展示,吸引了近500位计算机视觉领域学者研究员、算法工程师、业界人士报名参与,共赴这场计算机视觉技术交流的盛宴。

主讲嘉宾

金连文,华南理工大学二级教授、博士生导师。

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05-金连文-基于深度学习的文字识别 现状及展望(1).pdf
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最新论文

Unconstrained handwriting recognition is an essential task in document analysis. It is usually carried out in two steps. First, the document is segmented into text lines. Second, an Optical Character Recognition model is applied on these line images. We propose the Simple Predict & Align Network: an end-to-end recurrence-free Fully Convolutional Network performing OCR at paragraph level without any prior segmentation stage. The framework is as simple as the one used for the recognition of isolated lines and we achieve competitive results on three popular datasets: RIMES, IAM and READ 2016. The proposed model does not require any dataset adaptation, it can be trained from scratch, without segmentation labels, and it does not require line breaks in the transcription labels. Our code and trained model weights are available at https://github.com/FactoDeepLearning/SPAN.

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