Generating Structurally Complete Stylish Chinese Font Based on Semi-Supervised Model

Abstract

Stylish fonts are widely utilized in social networks, driving research interest in stylish Chinese font generation. Most existing methods for generating Chinese characters rely on GAN-based deep learning approaches. However, persistent issues of mode collapse and missing structure in CycleGAN-generated results make font generation a formidable task. This paper introduces a unique semi-supervised model specifically designed for generating stylish Chinese fonts. By incorporating a small amount of paired data and stroke encoding as information into CycleGAN, our model effectively captures both the global structural features and local characteristics of Chinese characters. Additionally, an attention module has been added and refined to improve the connection between strokes and character structures. To enhance the model’s understanding of the global structure of Chinese characters and stroke encoding reconstruction, two loss functions are included. The integration of these components successfully alleviates mode collapse and structural errors. To assess the effectiveness of our approach, comprehensive visual and quantitative assessments are conducted, comparing our method with benchmark approaches on six diverse datasets. The results clearly demonstrate the superior performance of our method in generating stylish Chinese fonts.