超强合集：OCR 文本检测干货汇总（含论文、源码、demo 等资源）

awesome-deep-text-detection-recognition

A curated list of awesome deep learning based papers on text detection and recognition.

资源链接： https://github.com/hwalsuklee/awesome-deep-text-detection-recognition

Papers

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: https://www.cs.stanford.edu/~acoates/papers/wangwucoatesng_icpr2012.pdf
• PhD thesis: https://cs.stanford.edu/people/dwu4/HonorThesis.pdf
  

Word Spotting and Recognition with Embedded Attributes

• paper: https://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: https://arxiv.org/abs/1412.1842
• homepage: https://www.robots.ox.ac.uk/~vgg/publications/2016/Jaderberg16/
• demo: https://zeus.robots.ox.ac.uk/textsearch/#/search/
• code: https://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: https://arxiv.org/abs/1412.5903
  

Deep Features for Text Spotting

• paper: https://www.robots.ox.ac.uk/~vgg/publications/2014/Jaderberg14/jaderberg14.pdf
• bitbucket: https://bitbucket.org/jaderberg/eccv2014_textspotting
• gitxiv: https://gitxiv.com/posts/uB4y7QdD5XquEJ69c/deep-features-for-text-spotting
  

Reading Scene Text in Deep Convolutional Sequences

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

DeepFont: Identify Your Font from An Image

• arxiv: https://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: https://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: https://arxiv.org/abs/1603.03101
• Writer-independent Feature Learning for Offline Signature Verification using Deep Convolutional Neural Networks
• arxiv: https://arxiv.org/abs/1604.00974
  

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

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

End-to-End Interpretation of the French Street Name Signs Dataset

• paper: https://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 Detection

Object Proposals for Text Extraction in the Wild

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

Text-Attentional Convolutional Neural Networks for Scene Text Detection

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

Accurate Text Localization in Natural Image with Cascaded Convolutional Text Network

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

Synthetic Data for Text Localisation in Natural Images

• intro: CVPR 2016
• project page: https://www.robots.ox.ac.uk/~vgg/data/scenetext/
• arxiv: https://arxiv.org/abs/1604.06646
• paper: https://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: https://arxiv.org/abs/1606.09002
  

Detecting Text in Natural Image with Connectionist Text Proposal Network

• intro: ECCV 2016
• arxiv: https://arxiv.org/abs/1609.03605
• github(Caffe): https://github.com/tianzhi0549/CTPN
• github(CUDA8.0 support): https://github.com/qingswu/CTPN
• demo: https://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: https://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: https://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

Text Recognition

Sequence to sequence learning for unconstrained scene text recognition

• intro: master thesis
• arxiv: https://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

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: https://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

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: https://www.cs.sjsu.edu/faculty/pollett/masters/Semesters/Spring15/geetika/CS298%20Slides%20-%20PDF
• github: https://github.com/bgeetika/Captcha-Decoder
• demo: https://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: https://simplecaptcha.sourceforge.net/
• code: https://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: https://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: https://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: https://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: https://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: https://www.cnblogs.com/charlotte77/p/5671136.html
  

LeNet – Convolutional Neural Network in Python

• blog: https://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: https://arxiv.org/abs/1604.03286
  

MLPaint: the Real-Time Handwritten Digit Recognizer

• blog: https://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: https://arxiv.org/abs/1601.05610
  

Number plate recognition with Tensorflow

• blog: https://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: https://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

Blogs

Applying OCR Technology for Receipt Recognition

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

Hacking MNIST in 30 lines of Python

• blog: https://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: https://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: https://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: https://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

WordPress媒体库识别.pdf文件的操作

WordPress的媒体库（Media Library）默认只支持图片、视频和音频，有时候这些是不够用的，媒体库允许上传的文件种类众多，需要更细化的分类，比如pdf文件

让媒体库支持pdf分类

这段来自tutsplus的代码可以帮助我们实现如上图所示的效果，将代码放到主题的functions.php中

代码如下

到媒体库中上传一个pdf文件，就可以看到效果了。

如何支持更多分类

WordPress支持的文件类型在wp_includes/functions.php中有写，搜索一下

代码如下:

就可以找到这些类型

代码如下:

找到自己需要的类型，按照

代码如下:

的写法，将‘application/pdf’替换成需要的mime类型即可，后面的文字也要相应改一改。这是php中增加数组成员的方式，你当然可以增加更多数组元素实现支持多个自定义类型。

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