Legacy Learning Strategy Based on Few-Shot Font Generation Models for Automatic Text Design in Metaverse Content

The metaverse consists of hardware, software, and content, among which text design plays a critical role in enhancing user immersion and usability as a content element. However, in languages such as Korean and Chinese that require thousands of unique glyphs, creating new text designs involves high costs and complexity. To address this, this study proposes a training strategy called Legacy Learning, which recombines and transforms structures based on existing text design models. This approach enables the generation of new text designs and improves quality without manual design processes. To evaluate Legacy Learning, it was applied to Korean and Chinese text designs. Additionally, we compared results before and after on seven state of the art text generation models. As a result, text designs generated using Legacy Learning showed over a 30% difference in Frechet Inception Distance (FID) and Learned Perceptual Image Patch Similarity (LPIPS) metrics compared to the originals, and also exhibited meaningful style variations in visual comparisons. Furthermore, the repeated learning process improved the structural consistency of the generated characters, and an OCR based evaluation showed increasing recognition accuracy across iterations, indicating improved legibility of the generated glyphs. In addition, a System Usability Scale (SUS) survey was conducted to evaluate usability among metaverse content designers and general users. The expert group recorded a score of 95.78 ("Best Imaginable"), while the non expert group scored 76.42 ("Excellent"), indicating an overall high level of usability. These results suggest that Legacy Learning can significantly improve both the production efficiency and quality of text design in the metaverse environment.