CCST-GAN: Generative Adversarial Networks for Chinese Calligraphy Style Transfer

Chinese calligraphy, a symbol of a traditional Chinese cultural heritage, serves not just as a writing tool but as vehicles for artistic expression. Each calligrapher's distinctive style embodies their individuality and the essence of their period. The advancement of computer vision techniques has spurred interest in both academic and artistic fields to study and reproduce these unique styles. This paper introduces a style transfer method for Chinese calligraphy, based on Generative Adversarial Networks (GAN), which accurately simulates the voids and brush strokes of calligraphy. Combining an enhanced generative adversarial network architecture with specially designed constraints and modules, this paper not only enhances the efficiency of style transfer but also achieves good results in visual effect, style coherence, and content authenticity. The experiments validate the outstanding performance of the designed model, and discuss its potential applications in artistic creation and cultural heritage, paving new paths for the study of Chinese character styles and digital art creation.