Typeface Completion with Generative Adversarial Networks
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The mood of a text and the intention of the writer can be reflected in the typeface. However, in designing a typeface, it is difficult to keep the style of various characters consistent, especially for languages with lots of morphological variations such as Chinese. In this paper, we propose a Typeface Completion Network (TCN) which takes a subset of characters as an input, and automatically completes the entire set of characters in the same style as the input characters. Unlike existing models proposed for style transfer, TCN embeds a character image into two separate vectors representing typeface and content. Combined with a reconstruction loss from the latent space, and with other various losses, TCN overcomes the inherent difficulty in designing a typeface. Also, compared to previous style transfer models, TCN generates high quality characters of the same typeface with a much smaller number of model parameters. We validate our proposed model on the Chinese and English character datasets, and the CelebA dataset on which TCN outperforms recently proposed state-ofthe-art models for style transfer. The source code of our model is available at https://github.com/yongqyu/TCN.
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