Automatic Generation of Chinese Vector Fonts via Deep Layout Inferring

Designing a high-quality Chinese vector font library which can be directly used in real applications is very time-consuming, since the font library typically consists of large amounts of glyphs. To address this problem, we propose a data-driven system in which only a small number (about 10%) of glyphs need to be designed. Specifically, the system first automatically decomposes those input glyphs into vectorized components. Then, a layout prediction module based on deep neural network is applied to learn the layout and structure information of input characters. Finally, proper components are selected to assemble each character based on the predicted layout to build the font library that can be directly used in computers and smart mobile devices. Experimental results demonstrate that our system synthesizes high-quality glyphs and significantly enhances the producing efficiency of Chinese vector fonts.