Learning to create multi-stylized Chinese character fonts by generative adversarial networks

Owing to the complex structure of Chinese characters and the huge number of Chinese characters, it is very challenging and time consuming for artists to design a new font of Chinese characters. Therefore, the generation of Chinese characters and the transformation of font styles have become research hotspots. At present, most of the models on Chinese character transformation cannot generate multiple fonts, and they are not doing well in faking fonts. In this paper, we propose a novel method of Chinese character fonts transformation and generation based on Generative Adversarial Networks. Our model is able to generate multiple fonts at once through font style specifying mechanism and it can generate a new font at the same time if we combine the characteristics of existing fonts.