Once the learning is finished, it can be used to infer the shape for the rest of characters. A neural network is trained to approximate the transformation in between two fonts given a subset of pairs of examples. Specifically, the whole font design process is formulated as a style transfer problem from a standard look font, such as SIMSUN, to an stylized target font. This project is an explorational take on this using deep learning. What about the designer just creates a subset of characters, then let computer figures out what the rest supposed to look like? After all, Chinese characters are consisting of a core set of radicals(偏旁部首), and the same radical looks pretty similar on different characters.
To make a GBK (a character set standardized by Chinese government) compatible font, designers will need to design unique looks for more than 26,000 Chinese characters, a daunting effort that could take years to complete. MotivationĬreating font is a hard business, creating a Chinese font is an even harder one. Please refer to the follow up zi2zi project for better result. Rewrite: Neural Style Transfer For Chinese Fonts
