Skeleton and Font Generation Network for Zero-shot Chinese Character Generation

TL;DR

This paper introduces a novel Skeleton and Font Generation Network (SFGN) for robust zero-shot Chinese character font generation, effectively handling subtle variations and misspelled characters, with applications in error correction.

Contribution

The paper presents a new approach that synthesizes content features independently of input images and aligns style at the radical level, improving font generation and error correction performance.

Findings

Outperforms state-of-the-art font generation methods.

Generates effective misspelled characters for data augmentation.

Enhances Chinese character error correction accuracy.

Abstract

Automatic font generation remains a challenging research issue, primarily due to the vast number of Chinese characters, each with unique and intricate structures. Our investigation of previous studies reveals inherent bias capable of causing structural changes in characters. Specifically, when generating a Chinese character similar to, but different from, those in the training samples, the bias is prone to either correcting or ignoring these subtle variations. To address this concern, we propose a novel Skeleton and Font Generation Network (SFGN) to achieve a more robust Chinese character font generation. Our approach includes a skeleton builder and font generator. The skeleton builder synthesizes content features using low-resource text input, enabling our technique to realize font generation independently of content image inputs. Unlike previous font generation methods that treat font style as a global embedding, we introduce a font generator to align content and style features on the radical level, which is a brand-new perspective for font generation. Except for common characters, we also conduct experiments on misspelled characters, a substantial portion of which slightly differs from the common ones. Our approach visually demonstrates the efficacy of generated images and outperforms current state-of-the-art font generation methods. Moreover, we believe that misspelled character generation have significant pedagogical implications and verify such supposition through experiments. We used generated misspelled characters as data augmentation in Chinese character error correction tasks, simulating the scenario where students learn handwritten Chinese characters with the help of misspelled characters. The significantly improved performance of error correction tasks demonstrates the effectiveness of our proposed approach and the value of misspelled character generation.