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Type-2 fuzzy Markov random fields to handwritten character recognition

  • Jia Zeng
  • , Zhi-Qiang Liu

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

Abstract

This paper integrate s Markov random fields (MRFs) with type-2 fuzzy sets (T2 FSs) referred to as T2 FMRFs, which can handle the fuzziness of the labeling space as well as the randomness of observations within the unified framework. Because fuzzy and random uncertainties exist in many computer vision problems, we extend the maximum a posteriori (MAP) criterion for the best labeling configuration by T2 FSs operations. We apply T2 FMRFs as character models to similar handwritten Chinese character recognition on ETL-9B and KAIST databases. Experimental results show that T2 FMRFs have a better classification and generalization ability for similar patterns than classical MRFs. © 2006 IEEE.
Original languageEnglish
Title of host publicationProceedings - 18th International Conference on Pattern Recognition, ICPR 2006
Pages1162-1165
Volume1
DOIs
Publication statusPublished - 2006
Event18th International Conference on Pattern Recognition, ICPR 2006 - Hong Kong, China
Duration: 20 Aug 200624 Aug 2006

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume1
ISSN (Print)1051-4651

Conference

Conference18th International Conference on Pattern Recognition, ICPR 2006
PlaceChina
CityHong Kong
Period20/08/0624/08/06

Bibliographical note

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