OCR 2014

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 deep neural networks and recurrent neural networks optical modeling for handwriting recognition[C]//International Conference on Statistical Language and Speech Processing. Springer, Cham, 2014: 199-210.
• Yao C, Bai X, Liu W. A unified framework for multioriented text detection and recognition[J]. IEEE Transactions on Image Processing, 2014, 23(11): 4737-4749.
• Huang W, Qiao Y, Tang X. Robust scene text detection with convolution neural network induced mser trees[C]//European Conference on Computer Vision. Springer, Cham, 2014: 497-511.
• Bhowmick S, Banerjee P. Bangla text recognition from video sequence: A new focus[J]. arXiv preprint arXiv:1401.1190, 2014.
• 【Synthetic data】Jaderberg M, Simonyan K, Vedaldi A, et al. Synthetic data and artificial neural networks for natural scene text recognition[J]. arXiv preprint arXiv:1406.2227, 2014.
  
        code:[model;offical website]
• Jaderberg M, Simonyan K, Vedaldi A, et al. Reading text in the wild with convolutional neural networks[J]. International Journal of Computer Vision, 2016, 116(1): 1-20.
  
        offical website:[offical website]
• Jaderberg M, Simonyan K, Vedaldi A, et al. Deep structured output learning for unconstrained text recognition[J]. arXiv preprint arXiv:1412.5903, 2014.