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.

我们提出了MetaScript，这是一个新颖的中文内容生成系统，旨在解决个人手写风格在中文字符的数字表示中逐渐消失的问题。我们的方法利用少样本学习的能力，生成既保留个人独特的手写风格又保持数字输入效率的中文字符。MetaScript在多种手写风格的多样数据集上进行训练，能够从最少的风格参考和标准字体中产生高质量的风格模仿。我们的工作为数字排版中保留书写交流中的个人特色提供了实用解决方案，特别是在中文书写环境中。值得注意的是，我们的系统在各种评估中表现出卓越的性能，包括识别准确率、初始分数和Frechet初始距离。同时，我们模型的训练条件易于满足，并有利于在实际应用中实现泛化。

元脚本：基于生成对抗网络的少样本手写中文内容生成