1. Masood, S.Z., Shu, G., Dehghan, A., Ortiz, E.G.: License plate detection and recognition using deeply learned convolutional neural networks. arXiv preprintarXiv:1703.07330 (2017)

2. Yang, F., Jin, L., Yang, W., Feng, Z., Zhang, S.: Handwritten/printed receipt classification using attention-based convolutional neural network. In: 15th International Conference on Frontiers in Handwriting Recognition, pp. 384–389 (2016)

   Google Scholar 

3. Ciregan, D., Meier, U., Schmidhuber, J.: Multi-column deep neural networks for image classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3642–3649 (2012)

   Google Scholar 

4. Yin, F., Wang, Q.F., Zhang, X.Y., Liu, C.L.: ICDAR 2013 Chinese handwriting recognition competition. In: 12th International Conference on Document Analysis and Recognition, pp. 1464–1470 (2013)

   Google Scholar 

5. Zhong, Z., Jin, L., Xie, Z.: High performance offline handwritten Chinese character recognition using GoogLeNet and directional feature maps. In: 13th International Conference on Document Analysis and Recognition, pp. 846–850 (2015)

   Google Scholar 

6. Zhang, X.-Y., Bengio, Y., Liu, C.-L.: Online and offline handwritten chinese character recognition: a comprehensive study and new benchmark. Pattern Recognit.61, 348–360 (2016)

   Article  Google Scholar 

7. Xiao, X., Jin, L., Yang, Y., Yang, W., Sun, J., Chang, T.: Building fast and compact convolutional neural networks for offline handwritten chinese character recognition. arXiv preprintarXiv:1702.07975 (2017)

   Article  Google Scholar 

8. Wachenfeld, S., Klein, H.U., Jiang, X.: Recognition of screen-rendered text. In: 18th International Conference on Pattern Recognition, pp. 1086–1089 (2006)

   Google Scholar 

9. Rashid, S.F., Shafait, F., Breuel, T.M.: An evaluation of HMM-based techniques for the recognition of screen rendered text. In: 11th International Conference on Document Analysis and Recognition, pp. 1260–1264 (2011)

   Google Scholar 

10. Wachenfeld, S., Klein, H.U., Jiang, X.: Annotated databases for the recognition of screen-rendered text. In: 9th International Conference on Document Analysis and Recognition, pp. 272–276 (2007)

    Google Scholar 

11. Bissacco, A., Cummins, M., Netzer, Y., Neven, H.: Photoocr: reading text in uncontrolled conditions. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 785–792 (2013)

    Google Scholar 

12. Jaderberg, M., Simonyan, K., Vedaldi, A., Zisserman, A.: Reading text in the wild with convolutional neural networks. Int. J. Comput. Vision116(1), 1–20 (2016)

    Article MathSciNet  Google Scholar 

13. Xu, X., Mu, N., Zhang, X., Li, B.: Covariance descriptor based convolution neural network for saliency computation in low contrast images. In: International Joint Conference on Neural Networks, pp. 616–623 (2016)

    Google Scholar 

14. Xu, X., Mu, N., Zhang, H., Fu, X.: Salient object detection from distinctive features in low contrast images. In: IEEE International Conference on Image Processing, pp. 3126–3130 (2015)

    Google Scholar 

15. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE86(11), 2278–2324 (1998)

    Article  Google Scholar 

16. Krizhevsky, A., Sutskever, I., Hinton, G.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems 25, pp. 1106–1114 (2012)

    Google Scholar 

17. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprintarXiv:1409.1556 (2015)

18. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)

    Google Scholar 

19. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. arXiv preprintarXiv:1512.03385 (2015)

20. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. arXiv preprintarXiv:1512.00567 (2015)

21. Jaderberg, M., Simonyan, K., Vedaldi, A., Zisserman, A.: Synthetic data and artificial neural networks for natural scene text recognition. arXiv preprintarXiv:1406.2227 (2014)

22. Ren, X., Chen, K., Sun, J.: A CNN based scene Chinese text recognition algorithm with synthetic data engine. arXiv preprintarXiv:1604.01891 (2016)

Download references