Each Convolutional layer is followed by a Batch Normalization layer then a ReLu layer, and zero padding all the way down.Network StructureĪfter trying various different architectures, including more sophisticated ones with residuals and deconvolution, I ended up with a more traditional flavour top-down CNN structure, as shown below. This project is heavily inspired by the awesome blog Analyzing 50k fonts using deep neural networks from Erik Bernhardsson, and great paper Learning Typographic Style from Shumeet Baluja. 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

Font chinese style