Abstract
With various styles, Chinese characters are one of the most important cultural symbols in China. Designing a set of new fonts with a specific style is a very tedious and massive task. It usually relies on traditional manual methods or computer-aided design for each Chinese character, which is time-consuming and labor-intensive. And the products are often not ideal. Therefore, it is necessary to design a model that can automatically generate new Chinese characters in a specified style. In this paper, a convolutional neural network model is proposed to be applied into the Chinese character style migration. To train the network, we exploit the root mean square optimizer to automatically adjust the deep learning rate and gradually reduce the difference values. Experiments are conducted based on the Chinese character datasets. Ultimately the resulting character style is basically close to the manually designed one, which reaches the target of the font style transfer.
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
Li, Y.N., Wang, P., Xiao, J.L.: An image style transfer algorithm using multi-dimensional histogram matching. Comput. Modern. 2, 15–18 (2019)
Gatys, L.A., Ecker, A.S., Bethge, M.: Image style transfer using convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2414–2423. IEEE, Las Vegas (2016)
Chen, D., Yuan, L., Liao, J., et al.: StyleBank: an explicit representation for neural image style transfer. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125–1134. IEEE, Honolulu, HI, USA (2017)
Isola, P., Zhu, J.Y., Zhou, T., et al.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5967–5976. IEEE, Honolulu, HI, USA (2017)
Zhu, J.Y., Park, T., Isola, P., et al.: Unpaired image-to-image translation using cycle-consistent adversarial networks. Proceedings of the IEEE international conference on computer vision, pp. 2223–2232. IEEE, Venice, Italy (2017)
Choi, Y., Choi, M., Kim, M., et al.: StarGAN: unified generative adversarial networks for multi-domain image-to-image translation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8789–8797. IEEE, Salt Lake City (2018)
Yang, S., Liu, J., Lian, Z., et al.: Awesome typography: statistics-based text effects transfer. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7464–7473. IEEE, Honolulu, HI, USA (2017)
Yang, S., Liu, J., Yang, W., et al.: Context-aware unsupervised text stylization. In: Proceedings of the 26th ACM international conference on Multimedia, pp. 1688–1696. ACM, Seoul in Korea (2018)
Zhang, X.Y., Yin, F., Zhang, Y.M., et al.: Drawing and recognizing Chinese characters with recurrent neural network. IEEE Trans. Patt. Anal. Mach. Intell. 99, 849–862 (2018)
Lyu, P., Bai, X., Yao, C., et al.: Auto-encoder guided GAN for Chinese calligraphy synthesis. In: The 14th IAPR International Conference on Document Analysis and Recognition, pp. 1095–1100. IEEE, Kyoto, Japan (2017)
Lu, S.Y., Hsiang, T.R.: Generating Chinese typographic and handwriting fonts from a small font sample set. In: 2018 International Joint Conference on Neural Networks, pp. 1–8. IEEE, Rio de Janeiro, Brazil (2018)
Baluja, S.: Learning typographic style: from discrimination to synthesis. Mach. Vis. Appl. 551–568 (2017). https://doi.org/10.1007/s00138-017-0842-6
Azadi, S., Fisher, M., et al.: Multi-content GAN for few-shot font style transfer. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7564–7573. IEEE, Salt Lake City, UT, USA (2018)
Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet classification with deep convolutional neural networks. Adv. Neural Inf. Proces. Syst. 25(2), 1–9 (2012)
Jiang, R.L., Liu, Y., et al.: Image based detection method of vibrating screen materials existence. J. Image Signal Process. 9(1), 57–64 (2020)
Jing, Y.L., Jiang, X., Ming, G.H.: Comparative study on several feature extraction methods of handwritten Chinese characters. Acad. Res. 30(10), 12–13 (2009)
Zhang, H.G., Guo, J., et al.: HCL2000: a large scale handwritten Chinese character database for handwritten character recognition. In: Proceedings of the 10th International Conference on Document Analysis and Recognition, pp. 286–290. IEEE, Barcelona, Spain (2009)
Yang, Y.L., Lin, X.S., et al.: Design and implementation of basic database for Chinese information processing and intelligent man-machine Interface in 863 Program. High-tech. Commun. 15(1), 107–110 (2005)
Jin, L.W., Gao, Y., et al.: CUT-COUCH2009: a comprehensive online unconstrained Chinese handwritten database and benchmark evaluation. Int. J. Doc. Anal. Recogn. 14, 53–54 (2011)
Li, J., Gao, J., et al.: Mongolian style transfer model based on convolutional Neural network. J. Inner Mongolia Agric. Univ. 42(5), 94–99 (2021)
Jiang, A.B., Wang, W.W.: Optimization of activation function ReLU. Sens. Microsyst. 37(2), 50–52 (2018)
Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Phy. D Nonlinear Phenom. 60(1–4), 259–268 (1992)
Huang, L., Li, W.B.: Generalization ability improvement method and application of BP network. J. Shijiazhuang Railway Inst. 18(3), 94–97 (2005)
Acknowledgement
This project was supported by National Natural Science Foundation of China Nos 61972059, 61773272, 61602332; Natural Science Foundation of the Jiangsu Higher Education Institutions of China No 19KJA230001, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University No 93K172016K08; the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). We gratefully acknowledge this support.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Chen, W., Liu, C., Ji, Y. (2022). Chinese Character Style Transfer Model Based on Convolutional Neural Network. In: Pimenidis, E., Angelov, P., Jayne, C., Papaleonidas, A., Aydin, M. (eds) Artificial Neural Networks and Machine Learning – ICANN 2022. ICANN 2022. Lecture Notes in Computer Science, vol 13532. Springer, Cham. https://doi.org/10.1007/978-3-031-15937-4_47
Download citation
DOI: https://doi.org/10.1007/978-3-031-15937-4_47
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-15936-7
Online ISBN: 978-3-031-15937-4
eBook Packages: Computer ScienceComputer Science (R0)