SPFont: Stroke potential features embedded GAN for Chinese calligraphy font generation

Abstract

Chinese calligraphy font generation is an extremely challenging problem. Firstly, Chinese calligraphy fonts have complex structures. The accuracy and artistic quality of the generated fonts will be affected by the order and layout of the strokes as well as the relationships between them. Secondly, the number of Chinese characters is large, but existing calligraphy works are scarce. Hence, it is difficult to establish a comprehensive and high-quality Chinese calligraphy dataset. In this paper, we propose an unsupervised calligraphy font generation network SPFont. It is based on a generative adversarial network (GAN) framework. The generator includes a style feature encoder, a content feature encoder, a stroke potential feature fusion module (SPFM) and a decoder. The SPFM module, by overlaying lower-level style and content features, better preserves fine details of the font such as stroke thickness, curve shapes and other characteristics. The SPFM module and the extracted style features are fused and then fed into the decoder, allowing it to consider the influence of style, content and stroke potential simultaneously during the generation process. Experimental results demonstrate that our model generates Chinese calligraphy fonts with higher quality compared to previous methods.