Glyph-Based Data Augmentation for Accurate Kanji Character Recognition

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

1 Citation (Scopus)

Abstract

In this paper, we address a problem of data augmentation for character recognition. Particularly, we focus on incorporating variation in glyph into data augmentation of character images, which is a simple approach for data augmentation. Generally, existing methods increase data size by distorting images, whereas the proposed method applies noise injection into glyphs, resulting in data with radical variation in glyph. The proposed method exploits public database of glyphs for kanji and augments glyphs by injecting noise into glyphs. Then, we generate images of kanji automatically by deploying stroke images on the augmented glyphs. We carried out experiments for kanji character recognition using augmented data. The results show the effectiveness of the proposed method.

Original languageEnglish
Title of host publicationProceedings - 14th IAPR International Conference on Document Analysis and Recognition, ICDAR 2017
PublisherIEEE Computer Society
Pages597-602
Number of pages6
ISBN (Electronic)9781538635865
DOIs
Publication statusPublished - 2017 Jul 2
Event14th IAPR International Conference on Document Analysis and Recognition, ICDAR 2017 - Kyoto, Japan
Duration: 2017 Nov 92017 Nov 15

Publication series

NameProceedings of the International Conference on Document Analysis and Recognition, ICDAR
Volume1
ISSN (Print)1520-5363

Other

Other14th IAPR International Conference on Document Analysis and Recognition, ICDAR 2017
CountryJapan
CityKyoto
Period17/11/917/11/15

Keywords

  • Character recognition
  • Data augmentation
  • Glyph

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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