Self-supervised Chinese font generation based on square-block transformation

Abstract

Listen

Translate article

The generation of Chinese fonts has attracted much attention due to its wide range of applications such as artistic font generation, personalized font design, and calligraphy generation. The predominated Chinese font generation methods are mainly based on unpaired data and deep generative models such as generative adversarial networks. However, the existing deep methods for Chinese font generation commonly ignore the special structure information of Chinese characters, resulting in the lack of guidance during the feature extraction, and usually suffer from the mode collapse issue during the training procedure. Thus, the generation quality of Chinese characters needs to be improved.In order to solve this problem, this paper proposes a squared-block transformation-based self-supervised method to guide the model network to extract features with higher quality, and thus the proposed method significantly improves the performance on the generation of Chinese character fonts. It should be pointed out that the suggested squared-block geometric transformation does not require any modification of existing model networks and any additional human-labor cost. Thus, it can be adapted to many existing deep generative models for Chinese font generation. The effectiveness of the suggested self-supervised method is demonstrated by a series of experiments.The experiment results show that when equipped with the suggested squared-block transformation-based method, the popular CycleGAN-based deep model for Chinese font generation can significantly improve the quality of generated characters and stabilize the training procedure, while reducing the mode collapse.Moreover, when compared to other existing deep methods, the proposed method also outperforms them in terms of four evaluation metrics such as the content accuracy, FID, $L_1$ loss, and IOU, as well as the quality of generated characters.