Generating Chinese Typographic and Handwriting Fonts from a Small Font Sample Set

Traditional Chinese is significantly different from other major languages, as compared to western countries use Latin alphabet letters system, Chinese contains over 13,000 common and sub-commonly used characters. Each of these Chinese characters is composed of multiple parts, which are arranged and combined according to various orthographies. Therefore, how to handle these enormous character counts and how to design of more efficient fonts are longstanding issues in the creation of Chinese fonts. In this study, we seek to address these issues using deep learning based on the concept of style transfer. Previous font generating methods generally require multiple pre-processing steps for feature extraction, and these cannot be simultaneously adapted to typographic and handwritten characters. In this study, we propose a neural network architecture for Chinese fonts conversion, which directly uses images as inputs. In this architecture, neural networks are trained using a small number of selected characters, which are then used to extract and convert the features of an input font. The outputs of the architecture are characters with identical content albeit different font styles. We apply our method to ten different font style including typographic and handwriting and demonstrate it capable of converting Chinese-character fonts into fonts that are similar to the original fonts.