RD-GAN: Few/Zero-Shot Chinese Character Style Transfer via Radical Decomposition and Rendering

Yaoxiong Huang, Mengchao He, Lianwen Jin, Yongpan Wang ;

Style transfer has attracted much interest owing to its various applications. Compared with English character or general artistic style transfer, Chinese character style transfer remains a challenge owing to the large size of the vocabulary(70224 characters in GB18010-2005) and the complexity of the structure. Recently some GAN-based methods were proposed for style transfer; however, they treated Chinese characters as a whole, ignoring the structures and radicals that compose characters. In this paper, a novel radical decomposition-and-rendering-based GAN(RD-GAN) is proposed to utilize the radical-level compositions of Chinese characters and achieves few-shot/zero-shot Chinese character style transfer. The RD-GAN consists of three components: a radical extraction module (REM), radical rendering module (RRM), and multi-level discriminator (MLD). Experiments demonstrate that our method has a powerful few-shot/zero-shot generalization ability by using the radical-level compositions of Chinese characters."