GlyphGAN: Style-consistent font generation based on generative adversarial networks

研究成果: Contribution to journalArticle査読

6 被引用数 (Scopus)

抄録

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.

本文言語英語
論文番号104927
ジャーナルKnowledge-Based Systems
186
DOI
出版ステータス出版済み - 12 15 2019

All Science Journal Classification (ASJC) codes

  • Management Information Systems
  • Software
  • Information Systems and Management
  • Artificial Intelligence

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