An intelligent font generation system based on stroke inference, mitigating production labor and enhancing design experience

Traditionally, font design has relied on manual craftsmanship by designers, a time-consuming and labor-intensive process that can take over a year to complete a new font family. Despite advancements in computer vision and graphics enabling the automation of font generation, creating high-quality fonts meeting commercial standards remains a technical challenge. Current automatic font generation technologies have not fully met production demands, mainly due to their lack of focus on generating glyphs that can be decomposed into strokes and their ineffective post-processing interaction, particularly for Chinese fonts. This study presents an innovative system for intelligently generating Chinese character fonts. The system utilizes a stroke database created by professional designers and combines font images generated through style transfer learning to perform stroke inference for font generation. The system’s core lies in its unique stroke inference mechanism, accurately identifying and matching strokes within font images to efficiently align with standard stroke data in the database. This approach not only improves the precision of font generation but also streamlines subsequent processing steps. Compared to traditional font design systems, our system shows significant advantages in generating fonts suitable for commercial use. It not only aids designers in enhancing work efficiency but also has the potential to greatly increase the production efficiency of font libraries. Moreover, the system’s design is scalable, offering extensive application prospects for future expansion to other East Asian scripts like Japanese and Korean.