Typeface Completion with Generative Adversarial Networks
This addresses the difficulty in designing consistent typefaces for designers and users, particularly for languages with high morphological variation, though it is incremental as it builds on existing image-to-image translation methods.
The paper tackles the problem of automatically generating consistent typefaces for languages with many characters, such as Chinese, by proposing a Typeface Completion Network (TCN) that embeds character images into separate typeface and content vectors, achieving state-of-the-art performance on Chinese, English, and CelebA datasets with fewer parameters than previous models.
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 one character as an input, and automatically completes the entire set of characters in the same style as the input characters. Unlike existing models proposed for image-to-image translation, 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 image-to-image translation models, TCN generates high quality character images of the same typeface with a much smaller number of model parameters. We validate our proposed model on the Chinese and English character datasets, which is paired data, and the CelebA dataset, which is unpaired data. In these datasets, TCN outperforms recently proposed state-of-the-art models for image-to-image translation. The source code of our model is available at https://github.com/yongqyu/TCN.