MetaScript: Few-Shot Handwritten Chinese Content Generation via Generative Adversarial Networks
This addresses the diminishing personal touch in digital Chinese typography, though it appears incremental as an application of existing GAN/few-shot methods to a specific domain.
The paper tackles the problem of preserving personal handwriting styles in digital Chinese characters by proposing MetaScript, a few-shot learning system that generates high-quality stylistic imitations from minimal references while maintaining typing efficiency, achieving superior performance in recognition accuracy, inception score, and Frechet inception distance.
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.