A Study on the Refining Handwritten Font by Mixing Font Styles
This work addresses readability issues in handwritten fonts for applications like document creation and assisting individuals with reading difficulties, but it is incremental as it builds on existing GAN-based font generation techniques.
The paper tackles the problem of poor readability in handwritten fonts by introducing FontFusionGAN (FFGAN), a method that combines handwritten and printed fonts using a GAN to generate legible and visually appealing fonts, significantly enhancing readability while preserving aesthetic qualities.
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.