CVFont: Synthesizing Chinese Vector Fonts via Deep Layout Inferring

Creating a high-quality Chinese vector font library, which can be directly used in real applications is time-consuming and costly, since the font library typically consists of large amounts of vector glyphs. To address this problem, we propose a data-driven system in which only a small number (about 10%) of Chinese glyphs need to be designed. Specifically, the system first automatically decomposes those input glyphs into vector components. Then, a layout prediction module based on deep neural networks is applied to learn the layout style of input characters. Finally, proper components are selected to assemble the glyph of each unseen 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.