整理编辑：gloomyfish
论文
01
代码语言：javascript复制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=AND(p_IS_Number:6940341)
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/10.1007/978-3-319-46604-0_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场景文字检测
02
代码语言：javascript复制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文本识别
03
代码语言：javascript复制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场景文字检测 识别
04
代码语言：javascript复制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验证码识别
05
用深度学习实现验证码识别
代码语言：javascript复制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 Slides - PDF
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车牌识别
06
Reading Car License Plates Using Deep Convolutional Neural Networks and LSTMs
代码语言：javascript复制arxiv: http://arxiv.org/abs/1601.05610
Number plate recognition with Tensorflowblog: 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
