Font creation using class discriminative deep convolutional generative adversarial networks

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 4th Asian Conference on Pattern Recognition, ACPR 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages238-243
Number of pages6
ISBN (Electronic)9781538633540
DOIs
Publication statusPublished - Dec 13 2018
Event4th Asian Conference on Pattern Recognition, ACPR 2017 - Nanjing, China
Duration: Nov 26 2017Nov 29 2017

Publication series

NameProceedings - 4th Asian Conference on Pattern Recognition, ACPR 2017

Other

Other4th Asian Conference on Pattern Recognition, ACPR 2017
CountryChina
CityNanjing
Period11/26/1711/29/17

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Signal Processing

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