Radical Constraint-Based Generative Adversarial Network for Handwritten Chinese Character Generation

Abstract

Generative adversarial networks (GANs) have been used as a solution to handwritten Chinese character automatic generation (HCCAG) in recent years. However, most existing GAN-based methods adopt a pixel-based strategy, which ignores the radical structure of Chinese characters. To achieve better HCCAG, a radical constraint-based GAN (RC-GAN) is proposed in this work. In the proposed method, a gated recurrent unit (GRU)-based radical learning network is designed to calculate the radical components among Chinese characters, and radical consistent loss is applied to train this module. Finally, the radical learning module is fused with a cyclic structure GAN to improve the performance of Chinese character generation. The experimental results show that compared with the existing GAN, the proposed method has better performance. Specifically, the proposed method can reduce the stroke error in the generated Chinese character images.