专栏首页AI算法与图像处理最全OCR相关资料整理

最全OCR相关资料整理

来源:https://handong1587.github.io/deep_learning/2015/10/09/ocr.html#papers 已向作者申请转载,欢迎大家来补充,贡献出自己的一份力~

导读

收藏从未停止,行动从未开始。

最近看到一个非常赞的OCR相关资源,收集从2015.10.9到现在的一些OCR文献,github项目和博客资源等

目前我已经将其搬运到自己的github上,欢迎大家通过issues来补充优质内容,后续希望也能补充更多其他方向的资源~

https://github.com/DWCTOD/awesome-computer-vision

Paper

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

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

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

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/

Deep structured output learning for unconstrained text recognition

  • intro: “propose an architecture consisting of a character sequence CNN and an N-gram encoding CNN which act on an input image in parallel and whose outputs are utilized along with a CRF model to recognize the text content present within the image.”
  • arxiv: http://arxiv.org/abs/1412.5903

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

Reading Scene Text in Deep Convolutional Sequences

  • intro: AAAI 2016
  • arxiv: http://arxiv.org/abs/1506.04395

DeepFont: Identify Your Font from An Image

  • arxiv: http://arxiv.org/abs/1507.03196

An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition

  • intro: Convolutional Recurrent Neural Network (CRNN)
  • arxiv: http://arxiv.org/abs/1507.05717
  • github: https://github.com/bgshih/crnn
  • github: https://github.com/meijieru/crnn.pytorch

Recursive Recurrent Nets with Attention Modeling for OCR in the Wild

  • arxiv: http://arxiv.org/abs/1603.03101

Writer-independent Feature Learning for Offline Signature Verification using Deep Convolutional Neural Networks

  • arxiv: http://arxiv.org/abs/1604.00974

DeepText: A Unified Framework for Text Proposal Generation and Text Detection in Natural Images

  • arxiv: http://arxiv.org/abs/1605.07314

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

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

Smart Library: Identifying Books in a Library using Richly Supervised Deep Scene Text Reading

  • arxiv: https://arxiv.org/abs/1611.07385

Improving Text Proposals for Scene Images with Fully Convolutional Networks

  • intro: Universitat Autonoma de Barcelona (UAB) & University of Florence
  • intro: International Conference on Pattern Recognition (ICPR) - DLPR (Deep Learning for Pattern Recognition) workshop
  • arxiv: https://arxiv.org/abs/1702.05089

Scene Text Eraser

https://arxiv.org/abs/1705.02772

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

Implicit Language Model in LSTM for OCR

https://arxiv.org/abs/1805.09441

Scene Text Magnifier

  • intro: ICDAR 2019
  • arxiv: https://arxiv.org/abs/1907.00693

Text Detection

Object Proposals for Text Extraction in the Wild

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

Text-Attentional Convolutional Neural Networks for Scene Text Detection

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

Accurate Text Localization in Natural Image with Cascaded Convolutional Text Network

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

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

Scene Text Detection via Holistic, Multi-Channel Prediction

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

Detecting Text in Natural Image with Connectionist Text Proposal Network

  • intro: ECCV 2016
  • arxiv: http://arxiv.org/abs/1609.03605
  • github(Caffe): https://github.com/tianzhi0549/CTPN
  • github(CUDA8.0 support): https://github.com/qingswu/CTPN
  • demo: http://textdet.com/
  • github(Tensorflow): https://github.com/eragonruan/text-detection-ctpn

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

  • intro: AAAI 2017
  • arxiv: https://arxiv.org/abs/1611.06779
  • github(Caffe): https://github.com/MhLiao/TextBoxes
  • github: https://github.com/xiaodiu2010/TextBoxes-TensorFlow

TextBoxes++: A Single-Shot Oriented Scene Text Detector

  • intro: TIP 2018. University of Science and Technology(HUST)
  • arxiv: https://arxiv.org/abs/1801.02765
  • github(official, Caffe): https://github.com/MhLiao/TextBoxes_plusplus

Arbitrary-Oriented Scene Text Detection via Rotation Proposals

  • intro: IEEE Transactions on Multimedia
  • keywords: RRPN
  • arxiv: https://arxiv.org/abs/1703.01086
  • github: https://github.com/mjq11302010044/RRPN
  • github: https://github.com/DetectionTeamUCAS/RRPN_Faster-RCNN_Tensorflow

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

Detecting Oriented Text in Natural Images by Linking Segments

  • intro: CVPR 2017
  • arxiv: https://arxiv.org/abs/1703.06520
  • github(Tensorflow): https://github.com/dengdan/seglink

Deep Direct Regression for Multi-Oriented Scene Text Detection

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

Cascaded Segmentation-Detection Networks for Word-Level Text Spotting

https://arxiv.org/abs/1704.00834

Text-Detection-using-py-faster-rcnn-framework

  • github: https://github.com/jugg1024/Text-Detection-with-FRCN

WordFence: Text Detection in Natural Images with Border Awareness

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

SSD-text detection: Text Detector

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

R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection

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

R-PHOC: Segmentation-Free Word Spotting using CNN

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

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

  • intro: ICCV 2017
  • arxiv: https://arxiv.org/abs/1707.03985

EAST: An Efficient and Accurate Scene Text Detector

  • intro: CVPR 2017. Megvii
  • arxiv: https://arxiv.org/abs/1704.03155
  • paper: http://openaccess.thecvf.com/content_cvpr_2017/papers/Zhou_EAST_An_Efficient_CVPR_2017_paper.pdf
  • github(Tensorflow): https://github.com/argman/EAST

Deep Scene Text Detection with Connected Component Proposals

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

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

Fused Text Segmentation Networks for Multi-oriented Scene Text Detection

https://arxiv.org/abs/1709.03272

Deep Residual Text Detection Network for Scene Text

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

Feature Enhancement Network: A Refined Scene Text Detector

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

ArbiText: Arbitrary-Oriented Text Detection in Unconstrained Scene

https://arxiv.org/abs/1711.11249

Detecting Curve Text in the Wild: New Dataset and New Solution

  • arxiv: https://arxiv.org/abs/1712.02170
  • github: https://github.com/Yuliang-Liu/Curve-Text-Detector

FOTS: Fast Oriented Text Spotting with a Unified Network

https://arxiv.org/abs/1801.01671

PixelLink: Detecting Scene Text via Instance Segmentation

  • intro: AAAI 2018
  • arxiv: https://arxiv.org/abs/1801.01315

PixelLink: Detecting Scene Text via Instance Segmentation

  • intro: AAAI 2018. Zhejiang University & Chinese Academy of Sciences
  • arxiv: https://arxiv.org/abs/1801.01315

Sliding Line Point Regression for Shape Robust Scene Text Detection

https://arxiv.org/abs/1801.09969

Multi-Oriented Scene Text Detection via Corner Localization and Region Segmentation

  • intro: CVPR 2018
  • arxiv: https://arxiv.org/abs/1802.08948

Single Shot TextSpotter with Explicit Alignment and Attention

  • intro: CVPR 2018
  • arxiv: https://arxiv.org/abs/1803.03474

Rotation-Sensitive Regression for Oriented Scene Text Detection

  • intro: CVPR 2018
  • arxiv: https://arxiv.org/abs/1803.05265

Detecting Multi-Oriented Text with Corner-based Region Proposals

  • arxiv: https://arxiv.org/abs/1804.02690
  • github: https://github.com/xhzdeng/crpn

An Anchor-Free Region Proposal Network for Faster R-CNN based Text Detection Approaches

https://arxiv.org/abs/1804.09003

IncepText: A New Inception-Text Module with Deformable PSROI Pooling for Multi-Oriented Scene Text Detection

  • intro: IJCAI 2018. Alibaba Group
  • arxiv: https://arxiv.org/abs/1805.01167

Boosting up Scene Text Detectors with Guided CNN

https://arxiv.org/abs/1805.04132

Shape Robust Text Detection with Progressive Scale Expansion Network

  • arxiv: https://arxiv.org/abs/1806.02559
  • github: https://github.com/whai362/PSENet

A Single Shot Text Detector with Scale-adaptive Anchors

https://arxiv.org/abs/1807.01884

TextSnake: A Flexible Representation for Detecting Text of Arbitrary Shapes

  • intro: ECCV 2018
  • arxiv: https://arxiv.org/abs/1807.01544

Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes

  • intro: ECCV 2018. Huazhong University of Science and Technology & Megvii (Face++) Technology
  • arxiv: https://arxiv.org/abs/1807.02242

Accurate Scene Text Detection through Border Semantics Awareness and Bootstrapping

  • intro: ECCV 2018
  • arxiv: https://arxiv.org/abs/1807.03547

TextContourNet: a Flexible and Effective Framework for Improving Scene Text Detection Architecture with a Multi-task Cascade

https://arxiv.org/abs/1809.03050

Correlation Propagation Networks for Scene Text Detection

https://arxiv.org/abs/1810.00304

Scene Text Detection with Supervised Pyramid Context Network

  • intro: AAAI 2019
  • arxiv: https://arxiv.org/abs/1811.08605

Improving Rotated Text Detection with Rotation Region Proposal Networks

https://arxiv.org/abs/1811.07031

Pixel-Anchor: A Fast Oriented Scene Text Detector with Combined Networks

https://arxiv.org/abs/1811.07432

Mask R-CNN with Pyramid Attention Network for Scene Text Detection

  • intro: WACV 2019
  • arxiv: https://arxiv.org/abs/1811.09058

TextField: Learning A Deep Direction Field for Irregular Scene Text Detection

  • intro: Huazhong University of Science and Technology (HUST) & Alibaba Group
  • arxiv: https://arxiv.org/abs/1812.01393

Detecting Text in the Wild with Deep Character Embedding Network

  • intro: ACCV 2018
  • intro: Baidu
  • arxiv: https://arxiv.org/abs/1901.00363

MSR: Multi-Scale Shape Regression for Scene Text Detection

https://arxiv.org/abs/1901.02596

Pyramid Mask Text Detector

  • intro: SenseTime & Beihang University & CUHK
  • arxiv: https://arxiv.org/abs/1903.11800

Shape Robust Text Detection with Progressive Scale Expansion Network

  • intro: CVPR 2019
  • arxiv: https://arxiv.org/abs/1903.12473

Tightness-aware Evaluation Protocol for Scene Text Detection

  • intro: CVPR 2019
  • arxiv: https://arxiv.org/abs/1904.00813
  • github: https://github.com/Yuliang-Liu/TIoU-metric

Character Region Awareness for Text Detection

  • intro: CVPR 2019
  • keywords: CRAFT: Character-Region Awareness For Text detection
  • arxiv: https://arxiv.org/abs/1904.01941
  • github(official): https://github.com/clovaai/CRAFT-pytorch

Towards End-to-End Text Spotting in Natural Scenes

  • intro: An extension of the work “Towards End-to-end Text Spotting with Convolutional Recurrent Neural Networks”, Proc. Int. Conf. Comp. Vision 2017
  • arxiv: https://arxiv.org/abs/1906.06013

A Single-Shot Arbitrarily-Shaped Text Detector based on Context Attended Multi-Task Learning

  • intro: ACM MM 2019
  • arxiv: https://arxiv.org/abs/1908.05498

Geometry Normalization Networks for Accurate Scene Text Detection

  • intro: ICCV 2019
  • arxiv: https://arxiv.org/abs/1909.00794

Text Recognition

Sequence to sequence learning for unconstrained scene text recognition

  • intro: master thesis
  • arxiv: http://arxiv.org/abs/1607.06125

Drawing and Recognizing Chinese Characters with Recurrent Neural Network

  • arxiv: https://arxiv.org/abs/1606.06539

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

Stroke Sequence-Dependent Deep Convolutional Neural Network for Online Handwritten Chinese Character Recognition

  • arxiv: https://arxiv.org/abs/1610.04057

Visual attention models for scene text recognition

https://arxiv.org/abs/1706.01487

Focusing Attention: Towards Accurate Text Recognition in Natural Images

  • intro: ICCV 2017
  • arxiv: https://arxiv.org/abs/1709.02054

Scene Text Recognition with Sliding Convolutional Character Models

https://arxiv.org/abs/1709.01727

AdaDNNs: Adaptive Ensemble of Deep Neural Networks for Scene Text Recognition

https://arxiv.org/abs/1710.03425

A New Hybrid-parameter Recurrent Neural Networks for Online Handwritten Chinese Character Recognition

https://arxiv.org/abs/1711.02809

AON: Towards Arbitrarily-Oriented Text Recognition

  • arxiv: https://arxiv.org/abs/1711.04226
  • github: https://github.com/huizhang0110/AON

Arbitrarily-Oriented Text Recognition

  • intro: A method used in ICDAR 2017 word recognition competitions
  • arxiv: https://arxiv.org/abs/1711.04226

SEE: Towards Semi-Supervised End-to-End Scene Text Recognition

https://arxiv.org/abs/1712.05404

Edit Probability for Scene Text Recognition

  • intro: Fudan University & Hikvision Research Institute
  • arxiv: https://arxiv.org/abs/1805.03384

SCAN: Sliding Convolutional Attention Network for Scene Text Recognition

https://arxiv.org/abs/1806.00578

Adaptive Adversarial Attack on Scene Text Recognition

  • intro: University of Florida
  • arxiv: https://arxiv.org/abs/1807.03326

ESIR: End-to-end Scene Text Recognition via Iterative Image Rectification

https://arxiv.org/abs/1812.05824

A Multi-Object Rectified Attention Network for Scene Text Recognition

  • intro: Pattern Recognition 2019
  • keywords: MORAN
  • arxiv: https://arxiv.org/abs/1901.03003

SAFE: Scale Aware Feature Encoder for Scene Text Recognition

  • intro: ACCV 2018
  • arxiv: https://arxiv.org/abs/1901.05770

A Simple and Robust Convolutional-Attention Network for Irregular Text Recognition

https://arxiv.org/abs/1904.01375

FACLSTM: ConvLSTM with Focused Attention for Scene Text Recognition

https://arxiv.org/abs/1904.09405

Text Detection + Recognition

STN-OCR: A single Neural Network for Text Detection and Text Recognition

  • arxiv: https://arxiv.org/abs/1707.08831
  • github(MXNet): https://github.com/Bartzi/stn-ocr

Deep TextSpotter: An End-to-End Trainable Scene Text Localization and Recognition Framework

  • intro: ICCV 2017
  • arxiv: http://openaccess.thecvf.com/content_ICCV_2017/papers/Busta_Deep_TextSpotter_An_ICCV_2017_paper.pdf

FOTS: Fast Oriented Text Spotting with a Unified Network

https://arxiv.org/abs/1801.01671

Single Shot TextSpotter with Explicit Alignment and Attention

An end-to-end TextSpotter with Explicit Alignment and Attention

  • intro: CVPR 2018
  • arxiv: https://arxiv.org/abs/1803.03474
  • github(official, Caffe): https://github.com/tonghe90/textspotter

Verisimilar Image Synthesis for Accurate Detection and Recognition of Texts in Scenes

  • intro: ECCV 2018
  • arxiv: https://arxiv.org/abs/1807.03021
  • github: https://github.com/fnzhan/Verisimilar-Image-Synthesis-for-Accurate-Detection-and-Recognition-of-Texts-in-Scenes

Scene Text Detection and Recognition: The Deep Learning Era

  • arxiv: https://arxiv.org/abs/1811.04256
  • gihtub: https://github.com/Jyouhou/SceneTextPapers

A Novel Integrated Framework for Learning both Text Detection and Recognition

  • intro: Alibaba
  • arxiv: https://arxiv.org/abs/1811.08611

Efficient Video Scene Text Spotting: Unifying Detection, Tracking, and Recognition

  • intro: Zhejiang University & Hikvision Research Institute
  • arxiv: https://arxiv.org/abs/1903.03299

A Multitask Network for Localization and Recognition of Text in Images

  • intro: ICDAR 2019
  • arxiv: https://arxiv.org/abs/1906.09266

GA-DAN: Geometry-Aware Domain Adaptation Network for Scene Text Detection and Recognition

  • intro: ICCV 2019
  • arxiv: https://arxiv.org/abs/1907.09653

Breaking Captcha

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

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

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

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/

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/

Reading irctc captchas with 95% accuracy using deep learning

  • github: https://github.com/arunpatala/captcha.irctc

端到端的OCR:基于CNN的实现

  • blog: http://blog.xlvector.net/2016-05/mxnet-ocr-cnn/

I Am Robot: (Deep) 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

SimGAN-Captcha

  • intro: Solve captcha without manually labeling a training set
  • github: https://github.com/rickyhan/SimGAN-Captcha

Handwritten Recognition

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

Recognize your handwritten numbers

https://medium.com/@o.kroeger/recognize-your-handwritten-numbers-3f007cbe46ff#.jllz62xgu

Handwritten Digit Recognition using Convolutional Neural Networks in Python with Keras

  • blog: http://machinelearningmastery.com/handwritten-digit-recognition-using-convolutional-neural-networks-python-keras/

MNIST Handwritten Digit Classifier

  • github: https://github.com/karandesai-96/digit-classifier

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

LeNet – Convolutional Neural Network in Python

  • blog: http://www.pyimagesearch.com/2016/08/01/lenet-convolutional-neural-network-in-python/

Scan, Attend and Read: End-to-End Handwritten Paragraph Recognition with MDLSTM Attention

  • arxiv: http://arxiv.org/abs/1604.03286

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

Training a Computer to Recognize Your Handwriting

https://medium.com/@annalyzin/training-a-computer-to-recognize-your-handwriting-24b808fb584#.gd4pb9jk2

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/

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

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

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

Handwritten digit string recognition by combination of residual network and RNN-CTC

https://arxiv.org/abs/1710.03112

Plate Recognition

Reading Car License Plates Using Deep Convolutional Neural Networks and LSTMs

  • arxiv: http://arxiv.org/abs/1601.05610

Number plate recognition with Tensorflow

  • blog: http://matthewearl.github.io/2016/05/06/cnn-anpr/
  • github(Deep ANPR): https://github.com/matthewearl/deep-anpr

end-to-end-for-plate-recognition

  • github: https://github.com/szad670401/end-to-end-for-chinese-plate-recognition

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

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

Adversarial Generation of Training Examples for Vehicle License Plate Recognition

https://arxiv.org/abs/1707.03124

Towards End-to-End Car License Plates Detection and Recognition with Deep Neural Networks

  • arxiv: https://arxiv.org/abs/1709.08828

Towards End-to-End License Plate Detection and Recognition: A Large Dataset and Baseline

  • paper: http://openaccess.thecvf.com/content_ECCV_2018/papers/Zhenbo_Xu_Towards_End-to-End_License_ECCV_2018_paper.pdf
  • github: https://github.com/detectRecog/CCPD
  • dataset: https://drive.google.com/file/d/1fFqCXjhk7vE9yLklpJurEwP9vdLZmrJd/view

High Accuracy Chinese Plate Recognition Framework

  • intro: 基于深度学习高性能中文车牌识别 High Performance Chinese License Plate Recognition Framework.
  • gihtub: https://github.com/zeusees/HyperLPR

LPRNet: License Plate Recognition via Deep Neural Networks

  • intrp=o: Intel IOTG Computer Vision Group
  • intro: works in real-time with recognition accuracy up to 95% for Chinese license plates: 3 ms/plate on nVIDIAR GeForceTMGTX 1080 and 1.3 ms/plate on IntelR CoreTMi7-6700K CPU.
  • arxiv: https://arxiv.org/abs/1806.10447

How many labeled license plates are needed?

  • intro: Chinese Conference on Pattern Recognition and Computer Vision
  • arxiv: https://arxiv.org/abs/1808.08410

An End-to-End Neural Network for Multi-line License Plate Recognition

  • intro: ICPR 2018
  • paper: https://sci-hub.se/10.1109/ICPR.2018.8546200#
  • github: https://github.com/deeplearningshare/multi-line-plate-recognition

Blogs

Applying OCR Technology for Receipt Recognition

  • blog: http://rnd.azoft.com/applying-ocr-technology-receipt-recognition/
  • mirror: http://pan.baidu.com/s/1qXQBQiC

Hacking MNIST in 30 lines of Python

  • blog: http://jrusev.github.io/post/hacking-mnist/
  • github: https://github.com/jrusev/simple-neural-networks

Optical Character Recognition Using One-Shot Learning, RNN, and TensorFlow

https://blog.altoros.com/optical-character-recognition-using-one-shot-learning-rnn-and-tensorflow.html

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/

Projects

ocropy: Python-based tools for document analysis and OCR

  • github: https://github.com/tmbdev/ocropy

Extracting text from an image using Ocropus

  • blog: http://www.danvk.org/2015/01/09/extracting-text-from-an-image-using-ocropus.html

CLSTM : A small C++ implementation of LSTM networks, focused on OCR

  • github: https://github.com/tmbdev/clstm

OCR text recognition using tensorflow with attention

  • github: https://github.com/pannous/caffe-ocr
  • github: https://github.com/pannous/tensorflow-ocr

Digit Recognition via CNN: digital meter numbers detection

  • github(caffe): https://github.com/SHUCV/digit

Attention-OCR: Visual Attention based OCR

  • github: 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

  • github: https://github.com/edward-zhu/umaru

Tesseract.js: Pure Javascript OCR for 62 Languages

  • homepage: http://tesseract.projectnaptha.com/
  • github: https://github.com/naptha/tesseract.js

DeepHCCR: Offline Handwritten Chinese Character Recognition based on GoogLeNet and AlexNet (With CaffeModel)

  • github: https://github.com/chongyangtao/DeepHCCR

deep ocr: make a better chinese character recognition OCR than tesseract

https://github.com/JinpengLI/deep_ocr

Practical Deep OCR for scene text using CTPN + CRNN

https://github.com/AKSHAYUBHAT/DeepVideoAnalytics/blob/master/notebooks/OCR/readme.md

Tensorflow-based CNN+LSTM trained with CTC-loss for OCR

https://github.com//weinman/cnn_lstm_ctc_ocr

SSD_scene-text-detection

  • github: https://github.com//chenxinpeng/SSD_scene_text_detection
  • blog: http://blog.csdn.net/u010167269/article/details/52563573

Videos

LSTMs for OCR

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

Resources

Deep Learning for OCR

https://github.com/hs105/Deep-Learning-for-OCR

Scene Text Localization & Recognition Resources

  • intro: A curated list of resources dedicated to scene text localization and recognition
  • github: https://github.com/chongyangtao/Awesome-Scene-Text-Recognition

Scene Text Localization & Recognition Resources

  • intro: 图像文本位置感知与识别的论文资源汇总
  • github: https://github.com/whitelok/image-text-localization-recognition/blob/master/README.zh-cn.md

awesome-ocr: A curated list of promising OCR resources

https://github.com/wanghaisheng/awesome-ocr

本文分享自微信公众号 - AI算法与图像处理(AI_study),作者:林锦彬

原文出处及转载信息见文内详细说明,如有侵权,请联系 yunjia_community@tencent.com 删除。

原始发表时间:2019-10-09

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