A Novel Fuzzy Classifier using Fuzzy LVQ to Recognize Online Persian Handwriting

Author

Baghshah, M. Soleymani ; Shouraki, S. Bagheri ; Kasaei, S.

Author_Institution

Dept. of Comput. Eng., Sharif Univ. of Technol., Tehran

Volume

1

fYear

0

fDate

0-0 0

Firstpage

1878

Lastpage

1883

Abstract

Fuzzy logic is a powerful tool to represent imprecise and irregular patterns. This paper presents a novel fuzzy approach for recognizing online Persian (Farsi) handwriting. In this approach, a fuzzy classifier is introduced that uses a combination of the fuzzy LVQ learning model and the expert knowledge. This method applies an FLVQ network to distinguish between the similar tokens that appear at the end of the strokes. For other tokens, fuzzy linguistic terms are used to describe their features. The purposed method was run on a database of Persian isolated handwritten characters and achieved a high recognition rate compared to other available approaches

Keywords

fuzzy set theory; handwriting recognition; learning (artificial intelligence); natural languages; pattern classification; vector quantisation; Farsi handwriting; expert knowledge; fuzzy LVQ learning; fuzzy classifier; fuzzy linguistic terms; fuzzy logic; online Persian handwriting recognition; Character recognition; Fuzzy logic; Handwriting recognition; Hidden Markov models; Natural languages; Pattern recognition; Power engineering and energy; Spatial databases; Stochastic processes; Writing; FLVQ; Fuzzy Rule-Based; Persian Handwriting; Recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Information and Communication Technologies, 2006. ICTTA '06. 2nd

Conference_Location

Damascus

Print_ISBN

0-7803-9521-2

Type

conf

DOI

10.1109/ICTTA.2006.1684675

Filename

1684675