Open AccessLearning one‐to‐many stylised Chinese character transformation and generation by generative adversarial networks
Author(s) -
Chen Jiefu,
Ji Yanli,
Chen Hua,
Xu Xing
Publication year - 2019
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2019.0009
Subject(s) - font , character (mathematics) , generative grammar , computer science , chinese characters , transformation (genetics) , adversarial system , artificial intelligence , natural language processing , style (visual arts) , character recognition , image (mathematics) , art , literature , mathematics , biochemistry , chemistry , geometry , gene
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.