Chinese Calligraphy Generation Based on Residual Dense Network

Abstract

Chinese calligraphy, as a writing art of Chinese characters, plays an important role in the inheritance of traditional culture. Compared with simple English fonts, the generation of Chinese calligraphy fonts is more challenging. This paper proposes Chinese calligraphy generation based on residual dense network. The generator network combines residual network structure and dense network structure, and uses local residual learning and global feature fusion to enhance feature reuse and information continuous transmission. The discriminator network adopts an autoencoder structure, and restores the similarity between the distributions by an approximate convergence strategy to achieve fast and stable training. At the same time, the structural similarity loss is introduced to quantify the similarity between the generated font image and the real font image. Experiments show that the proposed method achieves good calligraphy font generation effect, which effectively improves the quality of the generated font images and the rate of training.