OCR 2015

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, 2015: 428-432.
• Wang Z, Yang J, Jin H, et al. Deepfont: Identify your font from an image[C]//Proceedings of the 23rd ACM international conference on Multimedia. ACM, 2015: 451-459.
• Gomez L, Karatzas D. Object proposals for text extraction in the wild[C]//Document Analysis and Recognition (ICDAR), 2015 13th International Conference on. IEEE, 2015: 206-210.[code]
• Shi B, Yao C, Zhang C, et al. Automatic script identification in the wild[C]//Document Analysis and Recognition (ICDAR), 2015 13th International Conference on. IEEE, 2015: 531-535.
• Busta M, Neumann L, Matas J. Fastext: Efficient unconstrained scene text detector[C]//Proceedings of the IEEE International Conference on Computer Vision. 2015: 1206-1214.[code]
• Zhang Z, Shen W, Yao C, et al. Symmetry-based text line detection in natural scenes[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015: 2558-2567.
  
        code:[code]
• Ray A, Rajeswar S, Chaudhury S. A hypothesize-and-verify framework for text recognition using deep recurrent neural networks[C]//Document Analysis and Recognition (ICDAR), 2015 13th International Conference on. IEEE, 2015: 936-940.
• Neumann L, Matas J. Efficient scene text localization and recognition with local character refinement[C]//Document Analysis and Recognition (ICDAR), 2015 13th International Conference on. IEEE, 2015: 746-750.
• Visin F, Kastner K, Cho K, et al. Renet: A recurrent neural network based alternative to convolutional networks[J]. arXiv preprint arXiv:1505.00393, 2015.
• Zhong Z, Jin L, Xie Z. High performance offline handwritten chinese character recognition using googlenet and directional feature maps[C]//Document Analysis and Recognition (ICDAR), 2015 13th International Conference on. IEEE, 2015: 846-850.
  
        code:[code]
• 【CRNN】Shi B, Bai X, Yao C. An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition[J]. IEEE transactions on pattern analysis and machine intelligence, 2017, 39(11): 2298-2304.
  
        code:【1 - offical】; 【2 - crnn.pytorch】; 【3 - unfinished】; 【4 - crnn.pytorch-chinese】; 【5 - crnn+stn-tf】; 【6 - lstm+ctc】; 【7 - ctpn+crnn-merge-cannot-train】; 【8 - crnn-mnist-keras】; 【9 - crnn-tf】; 【10 - crnn-tf-could-be-better】; 【11 - crnn.mxnet】; 【12 - crnn-tf-estimators】; 【13 - crnn-attention-tf】; 【14 - crnn.caffe】; 【15 - chinese.ocr-ctpn+crnn-tf+pytorch】; 【16 - another.crnn-attentive pooling】; 【17 - crnn-tf-music】; 【18 - crnn-tf-developing】; 【19 - crnn-torch】; 【20 - crnn-tf-developing】; 【21 - chinese-ocr-keras】; 【22 - crnn-tf-developing】; 【23 - ctpn+crnn-cannot-train-7】; 【24 - crnn-pytorch】; 【25 - cnn+lstm+ctc-tf】; 【26 - crnn-tf-resnet]】;【27 - caffe_ocr】
• He T, Huang W, Qiao Y, et al. Text-attentional convolutional neural network for scene text detection[J]. IEEE transactions on image processing, 2016, 25(6): 2529-2541.
• Sahu D K, Sukhwani M. Sequence to sequence learning for optical character recognition[J]. arXiv preprint arXiv:1511.04176, 2015.
• Hosseini-Asl E, Guha A. Similarity-based Text Recognition by Deeply Supervised Siamese Network[J]. arXiv preprint arXiv:1511.04397, 2015.
• Wang D H, Wang H, Zhang D, et al. Robust Scene Text Recognition Using Sparse Coding based Features[J]. arXiv preprint arXiv:1512.08669, 2015.