Font Style Transfer Using Neural Style Transfer and Unsupervised Cross-domain Transfer

In this paper, we study about font generation and conversion. The previous methods dealt with characters as ones made of strokes. On the contrary, we extract features, which are equivalent to the strokes, from font images and texture or pattern images using deep learning, and transform the design pattern of font images. We expect that generation of original font such as hand written characters will be generated automatically by the proposed approach. In the experiments, we have created unique datasets such as a ketchup character image dataset and improve image generation quality and readability of character by combining neural style transfer with unsupervised cross-domain learning.