Application of deep learning on font design for ethnic minority writing systems

Background Due to the scarcity of font data, the unique writing systems of ethnic minorities are marginalized in font design and font library development, and the research and application of deep learning technologies to solve these problems face complicated challenges. Methods To explore font design and font library development in minority writing systems, this study used improved steps to convert font files into a unified format image files and constructed a glyph image dataset of a minority writing system. Based on a semantic skeleton of text, a method of optimizing content image selection was proposed. According to the research needs, generative adversarial networks (GANs)-based models and diffusion-based models (DMs) were preliminarily selected and pre-trained. The glyph images in the constructed dataset were input into the selected and pre-trained models to generate glyph images of the minority writing system with a new style. By comparing these two types of models, a strategy of improving efficiency and quality is proposed for glyph image generation. Results These efforts provide a basis for selecting image generation technology, diversifying and developing font libraries of minority writing systems, and fostering cross-cultural communication and dissemination.