  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.
