MetaScript: Few-Shot Handwritten Chinese Content Generation via Generative Adversarial Networks

TL;DR

MetaScript is a few-shot learning system using GANs to generate personalized Chinese handwriting styles, balancing style preservation with digital efficiency, and demonstrating high-quality results with minimal data.

Contribution

It introduces a novel GAN-based few-shot learning approach for Chinese handwriting style transfer, enabling personalized content generation with limited style references.

Findings

Achieves high recognition accuracy and quality scores.

Demonstrates effective style imitation from minimal data.

Facilitates practical application with easy training conditions.

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.