Neural style difference transfer and its application to font generation

Gantugs Atarsaikhan, Kenji Iwana Brian, Seiichi Uchida

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

Abstract

Designing fonts requires a great deal of time and effort. It requires professional skills, such as sketching, vectorizing, and image editing. Additionally, each letter has to be designed individually. In this paper, we introduce a method to create fonts automatically. In our proposed method, the difference of font styles between two different fonts is transferred to another font using neural style transfer. Neural style transfer is a method of stylizing the contents of an image with the styles of another image. We proposed a novel neural style difference and content difference loss for the neural style transfer. With these losses, new fonts can be generated by adding or removing font styles from a font. We provided experimental results with various combinations of input fonts and discussed limitations and future development for the proposed method.

Original languageEnglish
Title of host publicationDocument Analysis Systems - 14th IAPR International Workshop, DAS 2020, Proceedings
EditorsXiang Bai, Dimosthenis Karatzas, Daniel Lopresti
PublisherSpringer
Pages544-558
Number of pages15
ISBN (Print)9783030570576
DOIs
Publication statusPublished - 2020
Event14th IAPR International Workshop on Document Analysis Systems, DAS 2020 - Wuhan, China
Duration: Jul 26 2020Jul 29 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12116 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th IAPR International Workshop on Document Analysis Systems, DAS 2020
CountryChina
CityWuhan
Period7/26/207/29/20

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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