MetaScript: Few-Shot Handwritten Chinese Content Generation via
  Generative Adversarial Networks
Xiangyuan Xue Kailing Wang Jiazi Bu Qirui Li Zhiyuan Zhang
In this work, we propose MetaScript, a novel Chinese content generation
system designed to address the diminishing presence of personal handwriting
styles in the digital representation of Chinese characters. Our approach
harnesses the power of few-shot learning to generate Chinese characters that
not only retain the individual's unique handwriting style but also maintain the
efficiency of digital typing. Trained on a diverse dataset of handwritten
styles, MetaScript is adept at producing high-quality stylistic imitations from
minimal style references and standard fonts. Our work demonstrates a practical
solution to the challenges of digital typography in preserving the personal
touch in written communication, particularly in the context of Chinese script.
Notably, our system has demonstrated superior performance in various
evaluations, including recognition accuracy, inception score, and Frechet
inception distance. At the same time, the training conditions of our model are
easy to meet and facilitate generalization to real applications.