OCR 文本检测干货汇总（含论文、源码、demo 等资源）
本帖最后由 KLVE 于 2019-7-22 17:04 编辑OCR 文本检测干货汇总（含论文、源码、demo 等资源）
https://github.com/handong1587/handong1587.github.io/blob/master/_posts/deep_learning/2015-10-09-ocr.mPapersMulti-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 Attributeshttp://bbs.cvmart.net/uploads/images/201901/09/3/y9SxXBYyK2.jpg?imageView2/2/w/1240/h/0
[*]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 Networkshttp://bbs.cvmart.net/uploads/images/201901/09/3/CIcMBhu8YU.png?imageView2/2/w/1240/h/0
[*]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
Text DetectionObject 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 Imageshttp://bbs.cvmart.net/uploads/images/201901/09/3/ZT2RMllUiR.png?imageView2/2/w/1240/h/0
[*]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 Spottinghttps://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 Detectionhttps://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 Scenehttps://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 Networkhttps://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 Detectionhttps://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 Approacheshttps://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 CNNhttps://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 Anchorshttps://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 Cascadehttps://arxiv.org/abs/1809.03050
Correlation Propagation Networks for Scene Text Detectionhttps://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 Networkshttps://arxiv.org/abs/1811.07031
Pixel-Anchor: A Fast Oriented Scene Text Detector with Combined Networkshttps://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
Text RecognitionSequence 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 recognitionhttps://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 Modelshttps://arxiv.org/abs/1709.01727
AdaDNNs: Adaptive Ensemble of Deep Neural Networks for Scene Text Recognitionhttps://arxiv.org/abs/1710.03425
A New Hybrid-parameter Recurrent Neural Networks for Online Handwritten Chinese Character Recognitionhttps://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 Recognitionhttps://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 Recognitionhttps://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 Rectificationhttps://arxiv.org/abs/1812.05824
Text Detection + RecognitionSTN-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 Networkhttps://arxiv.org/abs/1801.01671
Single Shot TextSpotter with Explicit Alignment and AttentionAn 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
Breaking CaptchaUsing 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 RecognitionHigh 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 numbershttp://bbs.cvmart.net/uploads/images/201901/09/3/83akimYg5W.png?imageView2/2/w/1240/h/0https://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 Recognizerhttp://bbs.cvmart.net/uploads/images/201901/09/3/x9Ts1xwlGe.gif?imageView2/2/w/1240/h/0
[*]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 Handwritinghttps://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-CTChttps://arxiv.org/abs/1710.03112
Plate RecognitionReading Car License Plates Using Deep Convolutional Neural Networks and LSTMs
[*]arxiv: http://arxiv.org/abs/1601.05610
Number plate recognition with Tensorflowhttp://bbs.cvmart.net/uploads/images/201901/09/3/9uuHPxrpGD.jpg?imageView2/2/w/1240/h/0
[*]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 Recognitionhttps://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
BlogsApplying OCR Technology for Receipt Recognitionhttp://bbs.cvmart.net/uploads/images/201901/09/3/g5boqYNhGU.png?imageView2/2/w/1240/h/0
[*]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 TensorFlowhttps://blog.altoros.com/optical-character-recognition-using-one-shot-learning-rnn-and-tensorflow.html
Creating a Modern OCR Pipeline Using Computer Vision and Deep Learninghttps://blogs.dropbox.com/tech/2017/04/creating-a-modern-ocr-pipeline-using-computer-vision-and-deep-learning/
Projectsocropy: 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 detectionhttp://bbs.cvmart.net/uploads/images/201901/09/3/Wq2rahfrhH.jpg?imageView2/2/w/1240/h/0
[*]github(caffe): https://github.com/SHUCV/digit
Attention-OCR: Visual Attention based OCRhttp://bbs.cvmart.net/uploads/images/201901/09/3/5MdEpeIAJl.jpg?imageView2/2/w/1240/h/0
[*]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 Languageshttp://bbs.cvmart.net/uploads/images/201901/09/3/7XUMGmer0v.gif?imageView2/2/w/1240/h/0
[*]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 tesseracthttps://github.com/JinpengLI/deep_ocr
Practical Deep OCR for scene text using CTPN + CRNNhttps://github.com/AKSHAYUBHAT/DeepVideoAnalytics/blob/master/notebooks/OCR/readme.md
Tensorflow-based CNN+LSTM trained with CTC-loss for OCRhttps://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
VideosLSTMs for OCR
[*]youtube: https://www.youtube.com/watch?v=5vW8faXvnrc
ResourcesDeep Learning for OCRhttps://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 resourceshttps://github.com/wanghaisheng/awesome-ocr
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