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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VIP内容

主题: Graph-based Methods in Pattern Recognition and Document Image Analysis

简介: 模式识别和文档图像分析中的许多任务被公式化为图形匹配问题。尽管问题具有NP难性,但快速准确的收敛已在模式识别的广泛应用中取得了重大进展。因此,学习基于图的表示形式和相关技术是真正兴趣。在本教程中,我们将介绍用于获得不同应用程序的图形表示的许多方法。之后,我们将解释用于在图域中识别,分类,检测和许多其他任务的基于图的不同算法,方法和技术。我们将介绍最近的趋势,包括图卷积网络和图中的消息传递,重点介绍在各种模式识别问题中的应用,例如化学分子分类和网络图形表示中的检测。此外,除了这些算法在文档图像分析和识别(尤其是模式识别)领域的不同应用之外,还将提供相关经验。

嘉宾介绍: DUTTA Anjan是位于巴塞罗那计算机视觉中心的P-SPHERE项目下的Marie-Curie博士后。他于2014年获得巴塞罗那自治大学(UAB)的计算机科学博士学位。他是IJCV,IEEE TCYB,IEEE TNNLS,PR,PRL等期刊的定期审稿人,并经常担任BMVC,ICPR,ACPR和ICFHR等各种科学会议的程序委员会委员。他最近的研究兴趣围绕视觉对象的基于图形的表示和解决计算机视觉,模式识别和机器学习中各种任务的基于图形的算法。

Luqman Muhammad Muzzamil博士是文档图像分析,模式识别和计算机视觉的研究科学家。自2015年11月以来,卢克曼目前在拉罗谢尔大学(法国)的L3i实验室担任研究工程师。Luqman曾在波尔多生物信息学中心(波尔多生物信息中心)担任研究工程师,并在拉罗谢尔大学(法国)的L3i实验室担任Jean-Marc Ogier教授的博士后研究员。 Luqman拥有FrançoisRabelais的图尔大学(法国)和巴塞罗那的Autonoma大学(西班牙)的计算机科学博士学位。他的博士学位论文由Jean-Yves Ramel教授和Josep Llados教授共同指导。他的研究兴趣包括结构模式识别,文档图像分析,基于相机的文档分析和识别,图形识别,机器学习,计算机视觉,增强现实和仿生学。

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Deep Learning based methods have emerged as the indisputable leaders for virtually all image restoration tasks. Especially in the domain of microscopy images, various content-aware image restoration (CARE) approaches are now used to improve the interpretability of acquired data. Naturally, there are limitations to what can be restored in corrupted images, and like for all inverse problems, many potential solutions exist, and one of them must be chosen. Here, we propose DivNoising, a denoising approach based on fully convolutional variational autoencoders (VAEs), overcoming the problem of having to choose a single solution by predicting a whole distribution of denoised images. First we introduce a principled way of formulating the unsupervised denoising problem within the VAE framework by explicitly incorporating imaging noise models into the decoder. Our approach is fully unsupervised, only requiring noisy images and a suitable description of the imaging noise distribution. We show that such a noise model can either be measured, bootstrapped from noisy data, or co-learned during training. If desired, consensus predictions can be inferred from a set of DivNoising predictions, leading to competitive results with other unsupervised methods and, on occasion, even with the supervised state-of-the-art. DivNoising samples from the posterior enable a plethora of useful applications. We are (i) showing denoising results for 13 datasets, (ii) discussing how optical character recognition (OCR) applications can benefit from diverse predictions, and are (iii) demonstrating how instance cell segmentation improves when using diverse DivNoising predictions.

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Deep Learning based methods have emerged as the indisputable leaders for virtually all image restoration tasks. Especially in the domain of microscopy images, various content-aware image restoration (CARE) approaches are now used to improve the interpretability of acquired data. Naturally, there are limitations to what can be restored in corrupted images, and like for all inverse problems, many potential solutions exist, and one of them must be chosen. Here, we propose DivNoising, a denoising approach based on fully convolutional variational autoencoders (VAEs), overcoming the problem of having to choose a single solution by predicting a whole distribution of denoised images. First we introduce a principled way of formulating the unsupervised denoising problem within the VAE framework by explicitly incorporating imaging noise models into the decoder. Our approach is fully unsupervised, only requiring noisy images and a suitable description of the imaging noise distribution. We show that such a noise model can either be measured, bootstrapped from noisy data, or co-learned during training. If desired, consensus predictions can be inferred from a set of DivNoising predictions, leading to competitive results with other unsupervised methods and, on occasion, even with the supervised state-of-the-art. DivNoising samples from the posterior enable a plethora of useful applications. We are (i) showing denoising results for 13 datasets, (ii) discussing how optical character recognition (OCR) applications can benefit from diverse predictions, and are (iii) demonstrating how instance cell segmentation improves when using diverse DivNoising predictions.

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