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

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12351)


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.


GAN Style transfer Radical decomposition Few-Shot/Zero-Shot learning 



This research is supported in part by NSFC (Grant No.: 61936003), GD-NSF (no. 2017A030312006), Alibaba Innovative Research Foundation (no. D8200510), and Fundamental Research Funds for the Central Universities (no. D2190570).


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Electronic and Information EngineeringSouth China University of TechnologyGuangzhouChina
  2. 2.Alibaba GroupHangzhouChina

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