Extracting Features for the Linguistic Variables of Fuzzy Rules Using Hidden Markov Model

Azizah Suliman, Md Nasir Sulaiman, Mohamed Othman, Rahmita Wirza

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

Abstract

In classifying handwritten characters, the stages prior to the classification phase play a role as major as the classification itself. This research work will be classifying the characters using a syntactical classification method namely fuzzy logic but will use the statistical method of Hidden Markov Model as an approach in extracting features for the linguistic variables of the fuzzy rule-based system. In this paper the feature extraction method will be highlighted and detailed. The HMM Model of a variable to be used in the classification system will be discussed. Experimental results from a few sample images show that the proposed technique is both effective and efficient to be used in extracting features for the linguistic variables of fuzzy rules.

Original languageEnglish
Title of host publicationInternational Electronic Conference on Computer Science
EditorsGeorge Psihoyios, George Psihoyios, Theodore E. Simos, Theodore E. Simos
PublisherAmerican Institute of Physics Inc.
Pages30-33
Number of pages4
ISBN (Electronic)9780735405905
DOIs
Publication statusPublished - 01 Jan 2008
EventInternational e-Conference on Computer Science 2007, IeCCS 2007 -
Duration: 30 Nov 200710 Dec 2007

Publication series

NameAIP Conference Proceedings
Volume1060
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Other

OtherInternational e-Conference on Computer Science 2007, IeCCS 2007
Period30/11/0710/12/07

    Fingerprint

All Science Journal Classification (ASJC) codes

  • Physics and Astronomy(all)

Cite this

Suliman, A., Sulaiman, M. N., Othman, M., & Wirza, R. (2008). Extracting Features for the Linguistic Variables of Fuzzy Rules Using Hidden Markov Model. In G. Psihoyios, G. Psihoyios, T. E. Simos, & T. E. Simos (Eds.), International Electronic Conference on Computer Science (pp. 30-33). (AIP Conference Proceedings; Vol. 1060). American Institute of Physics Inc.. https://doi.org/10.1063/1.3037080