Robust Chinese character recognition by selection of binary-based and grayscale-based classifier

Yoshinobu Hotta, Jun Sun, Yutaka Katsuyama, Satoshi Naoi

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

1 Citation (Scopus)

Abstract

As the spread of digital videos, digital cameras, and camera phones, lots of researches are reported about degraded character recognition. It is found that while the grayscale-based classifier is powerful for degraded character, the performance for clear character is not so good as binary-based classifier. In this paper, a dynamic classifier selection method is proposed to combine the two classifiers based on an estimation of the degradation level and the recognition reliability of the input character images. Experimental results show that the proposed method can achieve better recognition performance than the two individual ones.

Original languageEnglish
Title of host publicationDocument Analysis Systems VII - 7th International Workshop, DAS 2006, Proceedings
Pages553-563
Number of pages11
DOIs
Publication statusPublished - 2006
Externally publishedYes
Event7th International Workshop on Document Analysis Systems, DAS 2006 - Nelson, New Zealand
Duration: 2006 Feb 132006 Feb 15

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3872 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference7th International Workshop on Document Analysis Systems, DAS 2006
CountryNew Zealand
CityNelson
Period06/2/1306/2/15

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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    Hotta, Y., Sun, J., Katsuyama, Y., & Naoi, S. (2006). Robust Chinese character recognition by selection of binary-based and grayscale-based classifier. In Document Analysis Systems VII - 7th International Workshop, DAS 2006, Proceedings (pp. 553-563). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3872 LNCS). https://doi.org/10.1007/11669487_49