Yeongjin Jo  Shinjin Kang  Beomjoo Seo  Sunyoung Kim

10.5626/JOK.2025.52.1.77

AI few-shot font generation Korean font generation font generation system image synthesis

As the demand for personalized font generation continues to rise, the need for a customized font generation system has become increasingly important. In this study, we designed and implemented a font generation system using VQ-Font, a AI-based model in the field of Few-Shot Font Generation. This system can produce complete font files from just a few input images, making it ideal for personalized font generation. We adapted the original Chinese-centric font generation model to fit the structural characteristics of Korean and collected a Korean font dataset to fine-tune the model. As a result, our proposed model outperformed existing Korean font generation models, as confirmed by comparative experiments. We also assessed the font generation speed of the enhanced model, demonstrating its potential for practical applications.