文字的检测与识别资源

持续更新中.......

【综述( Survey)】

[2016-TIP] Text Detection Tracking and Recognition in Video:A Comprehensive Survey [paper]

[2015-PAMI] Text Detection and Recognition in Imagery: A Survey [paper]

[2014-FCS] Scene Text Detection and Recognition: Recent Advances and Future Trends[paper]

【场景文字检测（Scene Text Detection）】

[201703-arXiv] Deep Direct Regression for Multi-Oriented
Scene Text Detection[paper]

[201703-arXiv]Arbitrary-Oriented
Scene Text Detection via Rotation Proposal [paper]

[201702-arXiv] Improving Text Proposal for Scene Images with Fully Convolutional Networks [paper]

[2017-CVPR]EAST: An
Efficient and Accurate Scene Text Detector [paper]

[2017-CVPR] Deep Matching
Prior Network: Toward Tighter Multi-oriented Text Detection [paper]

[2017-CVPR] Detecting Oriented Text in Natural Images by Linking Segments [paper]

[2017-AAAI] TextBoxes: A Fast TextDetector with a Single Deep Neural Network [paper][code]

[2016-ECCV] CTPN: Detecting Text in Natural Image with Connectionist Text Proposal Network[paper][code]


[2016-PHD-Thesis] Context Modeling for Semantic Text Matching and Scene Text Detection[paper]

[2016-IJCAI] Scene Text Detection in
Video by Learning Locally and Globally [paper]

[201606-arXiv] Scene Text Detection via Holistic, Multi-Channel Prediction [paper]

[2016-CVPR] Accurate Text Localization in Natural Image with Cascaded Convolutional TextNetwork [paper]

[2016-CVPR] Synthetic Data for Text Localization in Natural Images [paper]
[data][code]

[2016-CVPR] CannyText Detector: Fast and Robust Scene Text Localization Algorithm[paper]

[2016-CVPR] Multi-oriented text detection with fully convolutional network[paper][code]

[2016-IJCV] Reading Text in the Wild with Convolutional Neural Networks[paper][demo][homepage]

[2016-TIP] Text-Attentional Convolutional Neural Networks for scene Text Detection[paper]

[2016-IJDAR] TextCatcher: a method to detect curved and challenging text in natural scenes[paper]

[201605-arXiv] DeepText: A Unified Framework for Text Proposal Generation and Text Detection in Natural Images[paper][data]

[201601-arXiv] TextProposals: a Text-specific Selective Search Algorithm for Word Spotting in the Wild [paper][code]

[2015-TPAMI] Real-time Lexicon-free Scene Text Localization and Recognition[paper]

[2015-CVPR] Symmetry-Based Text Line Detector in Natural Scenes [paper][code]

[2015-ICCV] FASText: Efficient unconstrained scene text detector[paper][code]

[2015-ICDAR] Object Proposal for Text Extraction in the Wild[paper][code]

[2015-PHD-Thesis] Deep Learning for Text Spotting [paper]

[2014-ECCV] Deep Features for Text Spotting [paper][code][Homepage]

[2014-TPAMI] Robust Text Detection in Natural Scene Images[paper]

[2014-ECCV] Robust Text Detection with Convolution Neural Network Induced MSER Trees [paper]

[2013-ICCV] Photo OCR:Reading Text in Uncontrolled Conditions[paper]

[2012-CVPR] Real-time scne text localization
and recognition[paper][code]

[2010-CVPR] SWT: Detecting Text in Natural Scenes with Stroke Width Transform [paper]
[code][code2]

【自然场景中的文字识别(Scene Text Recognition)】

[2016-NIPS] Generative Shape Models: Joint Text Recognition and Segmentation with Very Little Training Data [paper]

[2016-AAAI] Reading Scene Text in Deep Convolutional Sequences [paper]

[2016-CVPR] Recursive Recurrent Nets with Attention Modeling for OCR in the Wild [paper]

[2016-CVPR] Robust Scene Text Recognition with Automatic Rectification[paper]

[2015-CoRR] An End-to-End Trainable Neural Network for Image-based Sequence Recognition and It's Application to Scene Text Recognition [paper][code]

[2015-ICDAR] Automatic Script Identification in the Wild [paper]

[2015-ICLR] Deep structured output learning for unconstrained text recognition [paper]

[2014-NIPS] Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition [paper]
[homepage][model]

[2014-TIP] A unified Framework for Multi-Oriented Text Detection and Recognition [paper]

[2013-CVPR]Scene
Text Recognition using Part-based Tree-structured Character Detection [paper]

[2012-CVPR]top-down
and bottom-up cues for scene text recognition [paper]

[2012-ICPR] End-to-End Text Recognition with CNN [pager][code]


【嵌入型文字的检测与识别(Embedded Text Detection and Recognition)】

[201704-TPAMI]  A Unified Framework for Tracking based Text Detection and Recognition from
Web Videos [paper]

[2017-AAAI] Detection and Recognition of Text Embedding in Online Images via Neural Context
Models [paper][code]

【手写体识别(Handwriting Recognition)】

[201704-TPAMI] Drawing
and Recognizing Chinese Characters with RNN [paper]

[201610-arXiv]Learning Spatial-Semantic Context with Fully Convolutional Recurrent Network for Online Handwritten Chinese Text Recognition [paper]

[201610-arXiv] Stroke Sequence-Dependent Deep Convolutional Neural Network
for Online Handwritten Chinese Character Recognition [paper]

[201606-arXiv] Drawing and Recognizing  Chinese Characters with RNN [paper]

201604-arXiv] Scan,Attend and Read: End-to-End Handwritten Paragraph Recognition
with MDLSTM Attention [paper][video]

[2015-ICDAR] High Performance Offline Handwritten Chinese Character Recognition
Using GoogLeNet and Directional Feature Maps[paper][code][code2]

【数据集(datasets)】

I. For scene text detection

1. COCO-Text [Homepage]

 63,686 images, 173,589 text instances, 3 fine-grained text attributes.

2.Synth-Text [Homepage]

800k thousand images; 8 million synthetic word instances

3. MSRA-TD500[Homepage]

500 (300 training + 200 testing) natural images that their resolution of the image vary 1296x864~1920x1280; Chinese , English or mixture of both

4. SVT[Homepage]

resolution images (average size 1260 × 860) (100 images for training and250 images for

5. KAIST [Homepage]

and outdoor scenes containing text

6. ICDAR系列

-ICDAR 2015 (1000 training images + 500 testing images)[Homepage]

-ICDAR2013 (229 + 233)  [Homepage]

-ICDAR2011 (229 + 255)  [Homepage]

-ICDAR2005 (1001 + 489)[Homepage]

-ICDAR2003 (181 + 251)   [Homepage]

II.
For Scene Text Recognition

1.  IIIT-5K [Homepage]

Each image is a cropped word image of scene text with case-insensitive labels

2. Synth-Word[Homepage]

million images covering 90k English words (2014 Oxford; VGG)

3. StanfordSynth[Homepage]

single-character images of 62 characters (0-9, a-z, A-Z)

4.SVHN[Homepage]

SVHN is obtained from house numbers in Google Street View images.(over
600,000 digit images)

5. KAIST

74K [Homepage]

 Over 74K images from natural images, as well as a set of synthetically generated characters

7. ICDAR系列

【参考】

[1] 

[2]

[3]http://blog.csdn.net/peaceinmind/article/details/51387367