Text-Conditioned Diffusion Model for High-Fidelity Korean Font Generation
This addresses the challenge of automatic font generation for complex languages like Korean, which is important for designers and linguists, though it is incremental as it builds on existing diffusion models and style encoders.
The paper tackles the problem of generating high-quality Korean fonts from a single reference image, particularly for handwritten and printed styles, by introducing a diffusion-based method that uses a text encoder for phonetic representations and perceptual loss, achieving accurate and detailed results across over 2000 characters and outperforming benchmark methods.
Automatic font generation (AFG) is the process of creating a new font using only a few examples of the style images. Generating fonts for complex languages like Korean and Chinese, particularly in handwritten styles, presents significant challenges. Traditional AFGs, like Generative adversarial networks (GANs) and Variational Auto-Encoders (VAEs), are usually unstable during training and often face mode collapse problems. They also struggle to capture fine details within font images. To address these problems, we present a diffusion-based AFG method which generates high-quality, diverse Korean font images using only a single reference image, focusing on handwritten and printed styles. Our approach refines noisy images incrementally, ensuring stable training and visually appealing results. A key innovation is our text encoder, which processes phonetic representations to generate accurate and contextually correct characters, even for unseen characters. We used a pre-trained style encoder from DG FONT to effectively and accurately encode the style images. To further enhance the generation quality, we used perceptual loss that guides the model to focus on the global style of generated images. Experimental results on over 2000 Korean characters demonstrate that our model consistently generates accurate and detailed font images and outperforms benchmark methods, making it a reliable tool for generating authentic Korean fonts across different styles.