Farsi handwritten character recognition with moment invariants

Author

Dehghan, Mehdi ; Faez, Karim

Author_Institution

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

Volume

2

fYear

1997

fDate

2-4 Jul 1997

Firstpage

507

Abstract

This paper introduces an experimental evaluation of the effectiveness of utilizing various moments as pattern features in recognition of the handwritten Farsi characters. The moments that have been used are Zernike moments, pseudo Zernike moments, and Legendre moments. We have used an unsupervised neural network (ART2) for this application, so that the clusters are formed only based on inherent properties of pattern features. The performance of classification is dependent on the moment order as well as the type of the moment invariant, but the classification error rate was below 10% in all cases. The pseudo Zernike moments of order 5 had the best performance among all the moment invariants. Its error rate and discrimination factor were 3.06% and 96.92% respectively

Keywords

ART neural nets; handwriting recognition; pattern classification; Farsi handwritten character recognition; Legendre moments; Zernike moments; classification error rate; classification performance; clusters; discrimination factor; error rate; inherent properties; moment invariant type; moment invariants; moment order; pattern features; pseudo Zernike moments; unsupervised neural network; Adaptive systems; Character recognition; Error analysis; Handwriting recognition; Neural networks; Pattern recognition; Polynomials; Resonance; Shape; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Digital Signal Processing Proceedings, 1997. DSP 97., 1997 13th International Conference on

Conference_Location

Santorini

Print_ISBN

0-7803-4137-6

Type

conf

DOI

10.1109/ICDSP.1997.628387

Filename

628387