Creating New Chinese Fonts based on Manifold Learning and Adversarial Networks

Yuan Guo Zhouhui Lian Yingmin Tang Jianguo Xiao

 Institute of Computer Science and Technology, Peking University, Beijing, P.R.China

 {guo-yuan, lianzhouhui, tangyingmin, xiaojianguo}at pku.edu.cn

Abstract

The design of fonts, especially Chinese fonts, is known as a tough task that requires considerable time and professional skills. In this paper, we propose a method to easily generate Chinese font libraries in new styles based on manifold learning and adversarial networks. Starting from a number of existing fonts that cover various styles, we firstly use convolutional neural networks to obtain the representation features of these fonts, and then build a font manifold via non-linear mapping. Using the font manifold, we can interpolate and move between those existing fonts to get new font features, which are then fed into a generative network learned via adversarial training to generate the whole new font libraries. Experimental results demonstrate that high-quality Chinese fonts in various new styles against existing ones can be efficiently generated using our method.

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Creating New Chinese Fonts based on Manifold Learning and Adversarial Networks   Yuan Guo, Zhouhui Lian, Yingmin Tang, Jianguo Xiao      [ Paper]

visits since March. 2018