A Study on the Refining Handwritten Font by Mixing Font Styles

TL;DR

This paper introduces FontFusionGAN, a GAN-based method that blends handwritten and printed fonts to enhance readability while maintaining aesthetic qualities, applicable across various languages and text-image tasks.

Contribution

The paper presents a novel GAN approach for combining handwritten and printed fonts, improving legibility without sacrificing style, and extending to multilingual and attribute control applications.

Findings

Significantly improves font readability.

Generates visually appealing mixed fonts.

Applicable to multilingual and attribute-controlled font tasks.

Abstract

Handwritten fonts have a distinct expressive character, but they are often difficult to read due to unclear or inconsistent handwriting. FontFusionGAN (FFGAN) is a novel method for improving handwritten fonts by combining them with printed fonts. Our method implements generative adversarial network (GAN) to generate font that mix the desirable features of handwritten and printed fonts. By training the GAN on a dataset of handwritten and printed fonts, it can generate legible and visually appealing font images. We apply our method to a dataset of handwritten fonts and demonstrate that it significantly enhances the readability of the original fonts while preserving their unique aesthetic. Our method has the potential to improve the readability of handwritten fonts, which would be helpful for a variety of applications including document creation, letter writing, and assisting individuals with reading and writing difficulties. In addition to addressing the difficulties of font creation for languages with complex character sets, our method is applicable to other text-image-related tasks, such as font attribute control and multilingual font style transfer.