Neural Font Style Transfer

Gantugs Atarsaikhan, Brian Kenji Iwana, Atsushi Narusawa, Keiji Yanai, Seiichi Uchida

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

4 Citations (Scopus)

Abstract

In this paper, we chose an approach to generate fonts by using neural style transfer. Neural style transfer uses Convolution Neural Networks(CNN) to transfer the style of one image to another. By modifying neural style transfer, we can achieve neural font style transfer. We also demonstrate the effects of using different weighted factors, character placements, and orientations. In addition, we show the results of using non-Latin alphabets, non-text patterns, and non-text images as style images. Finally, we provide insight into the characteristics of style transfer with fonts.

Original languageEnglish
Title of host publicationProceedings - 1st Workshop of Machine Learning under International Conference on Document Analysis and Recognition, ICDAR-WML 2017
PublisherIEEE Computer Society
Pages51-56
Number of pages6
ISBN (Electronic)9781538635865
DOIs
Publication statusPublished - Jan 25 2018
Event1st Workshop of Machine Learning under International Conference on Document Analysis and Recognition, ICDAR-WML 2017 - Kyoto, Japan
Duration: Nov 11 2017 → …

Publication series

NameProceedings of the International Conference on Document Analysis and Recognition, ICDAR
Volume5
ISSN (Print)1520-5363

Other

Other1st Workshop of Machine Learning under International Conference on Document Analysis and Recognition, ICDAR-WML 2017
CountryJapan
CityKyoto
Period11/11/17 → …

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

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  • Cite this

    Atarsaikhan, G., Iwana, B. K., Narusawa, A., Yanai, K., & Uchida, S. (2018). Neural Font Style Transfer. In Proceedings - 1st Workshop of Machine Learning under International Conference on Document Analysis and Recognition, ICDAR-WML 2017 (pp. 51-56). (Proceedings of the International Conference on Document Analysis and Recognition, ICDAR; Vol. 5). IEEE Computer Society. https://doi.org/10.1109/ICDAR.2017.328