Abstract
The transfer of Chinese character style is a method of transferring the original character style to other target characters written in different styles and generating the target characters with similar character styles as the original characters. This paper deeply analyzes the related research of character style transfer, summarizes the principle and main methods of character style transfer, and emphatically analyzes the latest progress of the in-depth learning method in the aspect of character style transfer. We finalize the report by the problems to be solved in this field and the future research direction.
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References
Strassmann, S.: Hairy brushes. In: Conference on Computer Graphics & Interactive Techniques (1986)
庞云阶, 尹丽娜. 计算机控制毛笔绘画系统 CCBPS. 计算机辅助设计与图形学学报 (1), 37–40 (1989)
Lee, J.: Simulating oriental black-ink painting. Comput. Graph. Appl. IEEE 19(3), 74–81 (1999)
Wong, H.T.F., Ip, H.H.S.: Virtual brush: a model-based synthesis of Chinese calligraphy. Comput. Graph. 24(1), 99–113 (2000)
张振庭. 计算机书法创作模拟与渲染研究[D]. 浙江大学 (2011)
Xu, S., Lau, F.C.M., Cheung, W.K., et al.: Automatic generation of artistic Chinese calligraphy. Intell. Syst. IEEE 20(3), 32–39 (2004)
俞凯. 计算机书法若干关键技术研究. 浙江大学 (2010)
Zhang, J., Mao, G., Lin, H., Yu, J., Zhou, C.: Outline font generating from images of ancient chinese calligraphy. Trans. Edutainment 5, 122–131 (2011). https://doi.org/10.1007/978-3-642-18452-9_10
Hunter, J., Lagoze, C., Giles, L., et al.: Proceedings of the 10th Annual Joint Conference on Digital Libraries
Velek, O., Liu, C.L., Nakagawa, M.: Generating realistic Kanji character images from on-line patterns (2001)
Gatys, L.A., Ecker, A.S., Bethge, M.: A neural algorithm of artistic style. Computer Science (2015)
Yuweiming70. Style_Migration_For_Artistic_Font_With_CNN (2017). https://github.com/yuweiming70/Style_Migration_For_Artistic_Font_With_CNN
Tian, Y.: ReWrite (2016). https://github.com/kaonashi-tyc/Rewrite/
Lian, Z., Zhao, B., Chen, X., Xiao, J.: EasyFont: a style learning-based system to easily build your large-scale handwriting fonts. ACM Trans. Graph. 38, 1–18 (2018). https://doi.org/10.1145/3213767
Gregor, K., Danihelka, I., Graves, A., et al.: DRAW: a recurrent neural network for image generation. Computer Science 1462–1471 (2015)
Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., et al.: Generative adversarial nets. In: International Conference on Neural Information Processing Systems. MIT Press (2014)
Azadi, S., Fisher, M., Kim, V., et al.: Multi-content GAN for few-shot font style transfer (2017)
Bhunia, A.K., Bhunia, A.K., Banerjee, P., et al.: Word level font-to-font image translation using convolutional recurrent generative adversarial networks (2018)
Tian, Y.: ReWrite (2017). https://github.com/kaonashi-tyc/zi2zi/
Isola, P., Zhu, J.Y., Zhou, T., et al.: Image-to-image translation with conditional adversarial networks (2016)
Johnson, M., Schuster, M., Le, Q.V., et al.: Google’s multilingual neural machine translation system: enabling zero-shot translation (2016)
Odena, A., Olah, C., Shlens, J.: Conditional image synthesis with auxiliary classifier GANs (2016)
Chang, J., Gu, Y.: Chinese typography transfer (2017)
Chang, J., Gu, Y., Zhang, Y.: Chinese typeface transformation with hierarchical adversarial network (2017)
Chang, B., Zhang, Q., Pan, S., et al.: Generating handwritten Chinese characters using CycleGAN. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE (2018)
Zhu, J.Y., Park, T., Isola, P., et al.: Unpaired image-to-image translation using cycle-consistent adversarial networks (2017)
He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition (2015)
Lyu, P., Bai, X., Yao, C., et al.: Auto-encoder guided GAN for Chinese calligraphy synthesis. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR). IEEE Computer Society (2017)
Lian, Z., Zhao, B., Xiao, J.: Automatic generation of large-scale handwriting fonts via style learning. In: SIGGRAPH Asia Technical Briefs. ACM (2016)
Jiang, Y., Lian, Z., Tang, Y., et al.: DCFont: an end-to-end deep Chinese font generation system. In: SIGGRAPH Asia 2017 Technical Briefs. ACM (2017)
Acknowledgement
This work is supported by The National Key Research and Development Program of China (No. 2017YFB1402104).
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Ma, Y., Dong, Y., Li, K., Ren, J., Geng, G., Zhou, M. (2019). A Survey of Chinese Character Style Transfer. In: Wang, Y., Huang, Q., Peng, Y. (eds) Image and Graphics Technologies and Applications. IGTA 2019. Communications in Computer and Information Science, vol 1043. Springer, Singapore. https://doi.org/10.1007/978-981-13-9917-6_38
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DOI: https://doi.org/10.1007/978-981-13-9917-6_38
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