%A Ruo-ran CHENG,Xiao-li ZHAO,Hao-jun ZHOU,Han-chen YE %T Review of Chinese font style transfer research based on deep learning %0 Journal Article %D 2022 %J Journal of ZheJiang University (Engineering Science) %R 10.3785/j.issn.1008-973X.2022.03.010 %P 510-519, 530 %V 56 %N 3 %U {https://www.zjujournals.com/eng/CN/abstract/article_44767.shtml} %8 2022-03-20 %X

The research works of Chinese font style transfer were classified according to different stages of research development. The traditional methods were briefly reviewed and the deep learning-based methods were combed and analyzed. The commonly used open data sets and evaluation criteria were introduced. The future research trends were expected from four aspects, which were to improve the generation quality, enhance personalized differences, reduce the number of training samples, and learn calligraphy font style.