Abstract
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.
Abstract (translated)
在这项工作中，我们提出了MetaScript，一种旨在解决中文手写风格在数字字符表示中逐渐减少的问题的新颖中文内容生成系统。我们的方法利用了少样本学习的力量，从极少的样本来生成中文字符，不仅保留了了个人的独特手写风格，还保持了数字打字的效率。通过训练在一个多样化的手写风格数据集中，MetaScript擅长从少量的样式参考和标准字体中产生高质量的文体模仿。我们的工作展示了在保留书面沟通的个人触感的数字排版挑战方面的一种实际解决方案，特别是在中文文本背景下。值得注意的是，我们的系统在各种评估中已经表现出卓越的性能，包括识别准确度、创意得分和弗雷歇创新距离。同时，我们模型的训练条件很容易满足，并有助于将模型扩展到真实应用场景。
URL
https://arxiv.org/abs/2312.16251
