Unsupervised classification of handwritten Farsi numerals using evolution strategies

Author

Sabaei, Masoud ; Faez, Karim

Author_Institution

Dept. of Electr. Eng., Amirkabir Univ. of Technol., Tehran, Iran

Volume

1

fYear

1997

fDate

4-4 Dec. 1997

Firstpage

403

Abstract

Moments and functions of moments have been utilized as pattern features in various applications to achieve invariant recognition of two-dimensional image patterns. This paper introduces an experimental evaluation of the effectiveness of utilizing orthogonal moments such as Zernike moments, pseudo Zernike moments, and Legendre moments in recognition of the handwritten Farsi numerals. We used evolution strategies (ESs) for clustering of handwritten Farsi numerals, so that the clusters are formed only based on the inherent properties of the pattern features. Considering the fact that the classification is unsupervised, the error rate is about 5% for moments of orders higher than 5. The pseudo Zernike moments of order of 5 have the best performance among all the moment invariants.

Keywords

feature extraction; handwriting recognition; image classification; image recognition; optimisation; unsupervised learning; 2D image patterns; Legendre moments; Zernike moments; clustering; error rate; evolution strategies; experimental evaluation; handwritten Farsi numerals recognition; invariant recognition; moment invariants; moments functions; optimisation; orthogonal moments; pattern features; pseudo-Zernike moments; unsupervised classification; Character recognition; Electronic switching systems; Error analysis; Feature extraction; Frequency; Handwriting recognition; Image recognition; Noise robustness; Polynomials; Shape;

fLanguage

English

Publisher

ieee

Conference_Titel

TENCON '97. IEEE Region 10 Annual Conference. Speech and Image Technologies for Computing and Telecommunications., Proceedings of IEEE

Conference_Location

Brisbane, Qld., Australia

Print_ISBN

0-7803-4365-4

Type

conf

DOI

10.1109/TENCON.1997.647341

Filename

647341