Font creation using class discriminative deep convolutional generative adversarial networks

Kotaro Abe, Brian Kenji Iwana, Viktor Gosta Holmer, Seiichi Uchida

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

7 被引用数 (Scopus)

抄録

In this research, we attempt to generate fonts automatically using a modification of a Deep Convolutional Generative Adversarial Network (DCGAN) by introducing class consideration. DCGANs are the application of generative adversarial networks (GAN) which make use of convolutional and deconvolutional layers to generate data through adversarial detection. The conventional GAN is comprised of two neural networks that work in series. Specifically, it approaches an unsupervised method of data generation with the use of a generative network whose output is fed into a second discriminative network. While DCGANs have been successful on natural images, we show its limited ability on font generation due to the high variation of fonts combined with the need of rigid structures of characters. We propose a class discriminative DCGAN which uses a classification network to work alongside the discriminative network to refine the generative network. This results of our experiment shows a dramatic improvement over the conventional DCGAN.

本文言語英語
ホスト出版物のタイトルProceedings - 4th Asian Conference on Pattern Recognition, ACPR 2017
出版社Institute of Electrical and Electronics Engineers Inc.
ページ238-243
ページ数6
ISBN(電子版)9781538633540
DOI
出版ステータス出版済み - 12 13 2018
イベント4th Asian Conference on Pattern Recognition, ACPR 2017 - Nanjing, 中国
継続期間: 11 26 201711 29 2017

出版物シリーズ

名前Proceedings - 4th Asian Conference on Pattern Recognition, ACPR 2017

その他

その他4th Asian Conference on Pattern Recognition, ACPR 2017
国/地域中国
CityNanjing
Period11/26/1711/29/17

All Science Journal Classification (ASJC) codes

  • 人工知能
  • コンピュータ ビジョンおよびパターン認識
  • 信号処理

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