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.
Abstract (translated)
手写字体具有独特的表现力，但由于笔迹不清楚或不一致，它们通常难以阅读。FontFusionGAN（FFGAN）是一种结合手写和印刷字体以改进手写字体的新方法。我们的方法利用生成对抗网络（GAN）来创建融合了手写和印刷字体优点的新型字体。通过在包含手写和印刷字体的数据集上训练GAN，它可以生成清晰且视觉效果吸引人的字体图像。我们将该方法应用于一个手写字体数据集，并证明它显著提升了原始字体的可读性，同时保留其独特的美学风格。 我们的方法有望提高手写字体的易读性，在文档创建、书信书写以及帮助阅读和写作有困难的人士等方面大有用处。此外，除了解决复杂字符集语言中字体创作的难题外，我们的方法还适用于其他与文本图像相关任务，例如控制字体属性及跨多种语言风格转换。
URL
https://arxiv.org/abs/2505.12834
