Abstract
The performance of handwritten Chinese character recognition (HCCR) has been greatly improved by using deep learning methods in recent years. But few people pay attention to the influence of writing style on it. In this paper, we aim to improve the performance of HCCR further by weakening the influence of different writing styles. We propose a writing style adversarial network (WSAN) which includes three parts: feature extractor, character classifier and writer classifier. In the training process, we first preprocess raw image with feature extractor. Afterwards, the learned features are fed into both the character classifier and the writer classifier. We apply joint optimization on the top of these two classifiers. Specifically, we minimize the loss value of the character classifier to achieve character recognition function. At the same time, we maximize the loss value of the writer classifier to reduce the influence of writing style in HCCR. The experimental results on CASIA-HWDB1.1 prove that the proposed WSAN has a promoting effect on HCCR. And the experiments on the offline HCCR competition dataset of ICDAR-2013 also give competitive results compared with other methods.
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
Similar content being viewed by others
References
Kimura, F., Takashina, K., Tsuruoka, S., Miyake, Y.: Modified quadratic discriminant functions and the application to chinese character recognition. IEEE Trans. Pattern Anal. Mach. Intell. 1, 149–153 (1987)
Cireşan, D., Meier, U.: Multi-column deep neural networks for offline handwritten Chinese character classification. In: 2015 International Joint Conference on Neural Networks (IJCNN), pp. 1–6. IEEE (2015)
Wu, C., Fan, W., He, Y., Sun, J., Naoi, S.: Handwritten character recognition by alternately trained relaxation convolutional neural network. In: 2014 14th International Conference on Frontiers in Handwriting Recognition, pp. 291–296. IEEE (2014)
Zhong, Z., Jin, L., Xie, Z.: High performance offline handwritten Chinese character recognition using Googlenet and directional feature maps. In: 2015 13th International Conference on Document Analysis and Recognition (ICDAR), pp. 846–850. IEEE (2015)
Zhang, X.Y., Bengio, Y., Liu, C.L.: Online and offline handwritten chinese character recognition: a comprehensive study and new benchmark. Pattern Recogn. 61, 348–360 (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)
Ganin, Y., et al.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17(1), 2030–2096 (2016)
Park, G., Im, W.: Image-text multi-modal representation learning by adversarial backpropagation. arXiv preprint arXiv:1612.08354 (2016)
Kim, Y.B., Stratos, K., Kim, D.: Adversarial adaptation of synthetic or stale data. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, vol. 1, Long Papers, pp. 1297–1307 (2017)
Liu, P., Qiu, X., Huang, X.: Adversarial multi-task learning for text classification. arXiv preprint arXiv:1704.05742 (2017)
Pei, Z., Cao, Z., Long, M., Wang, J.: Multi-adversarial domain adaptation (2018)
Lin, M., Chen, Q., Yan, S.: Network in network. arXiv preprint arXiv:1312.4400 (2013)
Liu, C.L., Yin, F., Wang, D.H., Wang, Q.F.: Online and offline handwritten chinese character recognition: benchmarking on new databases. Pattern Recogn. 46(1), 155–162 (2013)
Zhang, Y.: Deep convolutional network for handwritten chinese character recognition. Computer Science Department, Stanford University (2015)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
Li, Z., Teng, N., Jin, M., Lu, H.: Building efficient cnn architecture for offline handwritten chinese character recognition. Int. J. Document Anal. Recogn. (IJDAR) 21(4), 233–240 (2018)
Acknowledgement
The work is supported by Shanghai Natural Science Foundation (No. 19ZR1415900).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Liu, H., Lyu, S., Zhan, H., Lu, Y. (2019). Writing Style Adversarial Network for Handwritten Chinese Character Recognition. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Communications in Computer and Information Science, vol 1142. Springer, Cham. https://doi.org/10.1007/978-3-030-36808-1_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-36808-1_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-36807-4
Online ISBN: 978-3-030-36808-1
eBook Packages: Computer ScienceComputer Science (R0)