An End-to-End Generation Model for Chinese Calligraphy Characters Based on Dense Blocks and Capsule Network

Chinese calligraphy is a significant aspect of traditional culture, as it involves the art of writing Chinese characters. Despite the development of numerous deep learning models for generating calligraphy characters, the resulting outputs often suffer from issues related to stroke accuracy and stylistic consistency. To address these problems, an end-to-end generation model for Chinese calligraphy characters based on dense blocks and a capsule network is proposed. This model aims to solve issues such as redundant and broken strokes, twisted and deformed strokes, and dissimilarity with authentic ones. The generator of the model employs self-attention mechanisms and densely connected blocks to reduce redundant and broken strokes. The discriminator, on the other hand, consists of a capsule network and a fully connected network to reduce twisted and deformed strokes. Additionally, the loss function includes perceptual loss to enhance the similarity between the generated calligraphy characters and the authentic ones. To demonstrate the validity of the proposed model, we conducted comparison and ablation experiments on the datasets of Yan Zhenqing's regular script, Deng Shiru's clerical script, and Wang Xizhi's running script. The experimental results show that, compared to the comparison model, the proposed model improves SSIM by 0.07 on average, reduces MSE by 1.95 on average, and improves PSNR by 0.92 on average, which proves the effectiveness of the proposed model.