Abstract
Offline handwritten text recognition (HCTR) has been a long-standing research topic. To build robust and high-performance offline HCTR systems, it is natural to develop data preprocessing and augmentation techniques, which, however, have not been fully explored. In this paper, we propose a data preprocessing and augmentation pipeline and a CNN-ResLSTM model for high-performance offline HCTR. The data preprocessing and augmentation pipeline consists of three steps: training text sample generation, text sample preprocessing and text sample synthesis. The CNN-ResLSTM model is derived by introducing residual connections into the RNN part of the CRNN architecture. Experiments show that on the proposed CNN-ResLSTM, the data preprocessing and augmentation pipeline can effectively and robustly improve the system performance: On two standard benchmarks, namely the CASIA-HWDB and the ICDAR-2013 handwriting competition dataset, the proposed approach achieves state-of-the-art results with correct rates of 97.28% and 96.99%, respectively. Furthermore, to make our model more practical, we employ model acceleration and compression techniques to build a fast and compact model without sacrificing the accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chen, K., et al.: A compact CNN-DBLSTM based character model for online handwritten Chinese text recognition. In: International Conference on Document Analysis and Recognition (ICDAR), pp. 1068–1073 (2017)
Du, J., Wang, Z.R., Zhai, J.F., Hu, J.S.: Deep neural network based hidden Markov model for offline handwritten Chinese text recognition. In: International Conference on Pattern Recognition (ICPR), pp. 3428–3433. IEEE (2016)
Graves, A.: Supervised Sequence Labelling. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-24797-2
Graves, A., Jaitly, N.: Towards end-to-end speech recognition with recurrent neural networks. In: Proceedings of the 31st International Conference on Machine Learning, pp. 1764–1772 (2014)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Jia, Y., et al.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 675–678. ACM (2014)
Kim, Y.D., Park, E., Yoo, S., Choi, T., Yang, L., Shin, D.: Compression of deep convolutional neural networks for fast and low power mobile applications. arXiv preprint arXiv:1511.06530 (2015)
Liu, C.L., Yin, F., Wang, D.H., Wang, Q.F.: Casia online and offline Chinese handwriting databases. In: International Conference on Document Analysis and Recognition (ICDAR), pp. 37–41. IEEE (2011)
Messina, R., Louradour, J.: Segmentation-free handwritten Chinese text recognition with LSTM-RNN. In: International Conference on Document Analysis and Recognition (ICDAR), pp. 171–175. IEEE (2015)
Peng, D., Jin, L., Wu, Y., Wang, Z., Cai, M.: A fast and accurate fully convolutional network for end-to-end handwritten Chinese text segmentation and recognition. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 25–30. IEEE (2019)
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. IEEE Trans. Pattern Anal. Mach. Intell. 39(11), 2298–2304 (2017)
Sueiras, J., Ruiz, V., Sanchez, A., Velez, J.F.: Offline continuous handwriting recognition using sequence to sequence neural networks. Neurocomputing 289, 119–128 (2018)
Veit, A., Wilber, M.J., Belongie, S.: Residual networks behave like ensembles of relatively shallow networks. In: Advances in Neural Information Processing Systems, pp. 550–558 (2016)
Vinciarelli, A., Luettin, J.: A new normalization technique for cursive handwritten words. Pattern Recogn. Lett. 22(9), 1043–1050 (2001)
Wang, Q.F., Yin, F., Liu, C.L.: Handwritten chinese text recognition by integrating multiple contexts. IEEE Trans. Pattern Anal. Mach. Intell. 34(8), 1469–1481 (2011)
Wang, S., Chen, L., Xu, L., Fan, W., Sun, J., Naoi, S.: Deep knowledge training and heterogeneous CNN for handwritten Chinese text recognition. In: International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 84–89 (2017)
Wang, Z.R., Du, J., Wang, W.C., Zhai, J.F., Hu, J.S.: A comprehensive study of hybrid neural network hidden markov model for offline handwritten Chinese text recognition. Int. J. Doc. Anal. Recogn. (IJDAR) 21(4), 241–251 (2018)
Wu, Y.C., Yin, F., Liu, C.L.: Improving handwritten Chinese text recognition using neural network language models and convolutional neural network shape models. Pattern Recogn. 65, 251–264 (2017)
Wu, Y.C., Yin, F., Zhuo, C., Liu, C.L.: Handwritten Chinese text recognition using separable multi-dimensional recurrent neural network. In: International Conference on Document Analysis and Recognition (ICDAR), pp. 79–84 (2017)
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. Pattern Recogn. 72, 72–81 (2017)
Xie, Z., Sun, Z., Jin, L., Ni, H., Lyons, T.: Learning spatial-semantic context with fully convolutional recurrent network for online handwritten Chinese text recognition. IEEE Trans. Pattern Anal. Mach. Intell. 40(8), 1903–1917 (2017)
Yang, Y., et al.: Accelerating and compressing LSTM based model for online handwritten Chinese character recognition. In: International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 110–115. IEEE (2018)
Yin, F., Wang, Q.F., Zhang, X.Y., Liu, C.L.: ICDAR 2013 Chinese handwriting recognition competition. In: International Conference on Document Analysis and Recognition (ICDAR), pp. 1464–1470. IEEE (2013)
Zhou, X.D., Wang, D.H., Tian, F., Liu, C.L., Nakagawa, M.: Handwritten Chinese/Japanese text recognition using semi-Markov conditional random fields. IEEE Trans. Pattern Anal. Mach. Intell. 35(10), 2413–2426 (2013)
Zhou, X.D., Zhang, Y.M., Tian, F., Wang, H.A., Liu, C.L.: Minimum-risk training for semi-Markov conditional random fields with application to handwritten Chinese/Japanese text recognition. Pattern Recogn. 47(5), 1904–1916 (2014)
Acknowledgement
This research is supported in part by NSFC (Grant No. 61936003), the National Key Research and Development Program of China (No. 2016YFB1001405), GD-NSF (no. 2017A030312006), Guangdong Intellectual Property Office Project(2018-10-1).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Xie, C., Lai, S., Liao, Q., Jin, L. (2020). High Performance Offline Handwritten Chinese Text Recognition with a New Data Preprocessing and Augmentation Pipeline. In: Bai, X., Karatzas, D., Lopresti, D. (eds) Document Analysis Systems. DAS 2020. Lecture Notes in Computer Science(), vol 12116. Springer, Cham. https://doi.org/10.1007/978-3-030-57058-3_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-57058-3_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-57057-6
Online ISBN: 978-3-030-57058-3
eBook Packages: Computer ScienceComputer Science (R0)
