Learning one‐to‐many stylised Chinese character transformation and generation by generative adversarial networks. Issue 14 (1st December 2019)

• Record Type: Journal Article
• Authors: Chen, Jiefu; Ji, Yanli; Chen, Hua; Xu, Xing
• Is Part Of: IET image processing. Volume 13:Issue 14(2019)
• Page Start: 2680
• Page End: 2686
• Publication Date: 2019-12-01
• Subjects: character sets -- optical character recognition -- document image processing -- learning (artificial intelligence) -- feature extraction -- natural language processing; stylised Chinese character transformation -- generative adversarial networks -- Chinese character fonts transformation -- font style‐specifying mechanism; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2019.0009
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Owing to the complex structure of Chinese characters and the huge number of Chinese characters, it is very challenging and time consuming for artists to design a new font of Chinese characters. Therefore, the generation of Chinese characters and the transformation of font styles have become research hotspots. At present, most of the models on Chinese character transformation cannot generate multiple fonts, and they are not doing well in faking fonts. In this article, the authors propose a novel method of Chinese character fonts transformation and generation based on generative adversarial networks. The authors' model is able to generate multiple fonts at once through font style‐specifying mechanism and it can generate a new font at the same time if the authors combine the characteristics of existing fonts.