GlyphGAN: Style-Consistent Font Generation Based on Generative Adversarial Networks
This addresses the need for automated font creation for designers and typographers, but it is incremental as it builds on existing GAN methods.
The paper tackled the problem of generating new fonts with style consistency across characters using Generative Adversarial Networks (GANs), resulting in fonts that maintain legibility and diversity while differing from training images.
In this paper, we propose GlyphGAN: style-consistent font generation based on generative adversarial networks (GANs). GANs are a framework for learning a generative model using a system of two neural networks competing with each other. One network generates synthetic images from random input vectors, and the other discriminates between synthetic and real images. The motivation of this study is to create new fonts using the GAN framework while maintaining style consistency over all characters. In GlyphGAN, the input vector for the generator network consists of two vectors: character class vector and style vector. The former is a one-hot vector and is associated with the character class of each sample image during training. The latter is a uniform random vector without supervised information. In this way, GlyphGAN can generate an infinite variety of fonts with the character and style independently controlled. Experimental results showed that fonts generated by GlyphGAN have style consistency and diversity different from the training images without losing their legibility.