Automatic Transformation of Korean Fonts using Unbalanced U-net and Generative Adversarial Networks

Pangjia, Seunghyun Ko, Yang Fang, Geun-sik Jo

http://doi.org/10.5626/JOK.2019.46.1.15

In this paper, we study the typography transfer problem: transferring a source font, to an analog font with a specified style. To solve the typography transfer problem, we treat the problem as an image-to-image translation problem, and propose an unbalanced u-net architecture based on Generative Adversarial Network(GAN). Unlike traditional balanced u-net architecture, architecture we proposed consists of two subnets: (1) an unbalanced u-net is responsible for transferring specified fonts style to another, while maintaining semantic and structure information; (2) an adversarial net. Our model uses a compound loss function that includes a L1 loss, a constant loss, and a binary GAN loss to facilitate generating desired target fonts. Experiments demonstrate that our proposed network leads to more stable training loss, with faster convergence speed in cheat loss, and avoids falling into a degradation problem in generating loss than balanced u-net.