ocr

Datasets there are three websites that have the dataset list of some different data type: 1 - www.iapr-tc11.org 2 - tc11.cvc.uab.es 3 - rrc.cvc.uab.es 2017 COCO-Text 2017 DeTEXT 2017 DOST 2017 FSNS 2017 MLT 2017 IEHHR 2011-2015 Born-DIgitalImage 2013-2015 Focused Scene Text 2013-2015 Text in Videos 2015 Incidental Scene Text ICDAR Chinese 2017 more than 12,000 images. Most of the images are collected in the wild by phone cameras. Task: Chinese Text in the Wild. Chinese Text in the Wild 2017 32,285 high resolution images, 1,018,402 character instances, 3,850 character categories, 6 kinds of

2019 Jiaming Liu, Chengquan Zhang, Yipeng Sun, Junyu Han, Errui Ding . Detecting Text in the Wild with Deep Character Embedding Network .[J] arXiv preprint arXiv:1901.00363. Chuhui Xue, Shijian Lu, Wei Zhang . MSR: Multi-Scale Shape Regression for Scene Text Detection .[J] arXiv preprint arXiv:1901.02596. 【MORAN】Canjie Luo, Lianwen Jin, Zenghui Sun . A Multi-Object Rectified Attention Network for Scene Text Recognition .[J] arXiv preprint arXiv:1901.03003. [code: Canjie-Luo/MORAN_v2 ] Wei Liu, Chaofeng Chen, Kwan-Yee K. Wong . SAFE: Scale Aware Feature Encoder for Scene Text Recognition .[J]

2017 Kil T, Seo W, Koo H I, et al. Robust Document Image Dewarping Method Using Text-Lines and Line Segments[C]//2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2017, 1: 865-870. [code: xellows1305/Document-Image-Dewarping ] Raj D, SAHU S, Anand A. Learning local and global contexts using a convolutional recurrent network model for relation classification in biomedical text [C]//Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017). 2017: 311-321. code :[ code ] Florian Fink, Klaus-U. Schulz, Uwe Springmann.

2016 Yin X C, Zuo Z Y, Tian S, et al. Text detection, tracking and recognition in video: a comprehensive survey[J]. IEEE Transactions on Image Processing, 2016, 25(6): 2752-2773. Zhu Y, Yao C, Bai X. Scene text detection and recognition: Recent advances and future trends [J]. Frontiers of Computer Science, 2016, 10(1): 19-36. He P, Huang W, Qiao Y, et al. Reading Scene Text in Deep Convolutional Sequences [C]//AAAI. 2016: 3501-3508. code :[ code ] Lee C Y, Osindero S. Recursive recurrent nets with attention modeling for OCR in the wild [C]//Proceedings of the IEEE Conference on Computer Vision

2015 Kim B S, Koo H I, Cho N I. Document dewarping via text-line based optimization[J]. Pattern Recognition, 2015, 48(11): 3600-3614. Ye Q, Doermann D. Text detection and recognition in imagery: A survey[J]. IEEE transactions on pattern analysis and machine intelligence, 2015, 37(7): 1480-1500. Jaderberg M. Deep learning for text spotting[D]. University of Oxford, 2015. Ren X, Chen K, Yang X, et al. A new unsupervised convolutional neural network model for Chinese scene text detection[C]//Signal and Information Processing (ChinaSIP), 2015 IEEE China Summit and International Conference on. IEEE

2014 Bušta M, Drtina T, Helekal D, et al. Efficient character skew rectification in scene text images [C]//Asian Conference on Computer Vision. Springer, Cham, 2014: 134-146. Almazán J, Gordo A, Fornés A, et al. Word spotting and recognition with embedded attributes [J]. IEEE transactions on pattern analysis and machine intelligence, 2014, 36(12): 2552-2566. code :[ code ] Jaderberg M, Vedaldi A, Zisserman A. Deep features for text spotting [C]//European conference on computer vision. Springer, Cham, 2014: 512-528. code :[ code ] Bluche T, Ney H, Kermorvant C. A comparison of sequence-trained

2013 Yin X C, Yin X, Huang K, et al. Robust text detection in natural scene images [J]. IEEE transactions on pattern analysis and machine intelligence, 2014, 36(5): 970-983. Bissacco A, Cummins M, Netzer Y, et al. Photoocr: Reading text in uncontrolled conditions [C]//Proceedings of the IEEE International Conference on Computer Vision. 2013: 785-792. Breuel T M, Ul-Hasan A, Al-Azawi M A, et al. High-performance OCR for printed English and Fraktur using LSTM networks [C]//Document Analysis and Recognition (ICDAR), 2013 12th International Conference on. IEEE, 2013: 683-687. code :[ code ]

2012 【Synthetic data】Wang T, Wu D J, Coates A, et al. End-to-end text recognition with convolutional neural networks [C]//Pattern Recognition (ICPR), 2012 21st International Conference on. IEEE, 2012: 3304-3308. code :[ code ] Elagouni K, Garcia C, Mamalet F, et al. Text recognition in videos using a recurrent connectionist approach [C]//International Conference on Artificial Neural Networks. Springer, Berlin, Heidelberg, 2012: 172-179. Frinken V, Fischer A, Manmatha R, et al. A novel word spotting method based on recurrent neural networks [J]. IEEE transactions on pattern analysis and machine