Combining Zernike moments with Regional features for classification of handwritten ancient Tamil scripts using Extreme Learning Machine

Author

Sridevi, N. ; Subashini, P.

Author_Institution

Dept. of Comput. Sci., Avinashilingam Univ. for Women, Coimbatore, India

fYear

2013

fDate

25-26 March 2013

Firstpage

158

Lastpage

162

Abstract

Handwritten Tamil character recognition is one of the active areas in research. Due to high variability of writing styles, developing handwritten character recognition system is a big challenge. The concept proposed gives a way to perform classification of handwritten ancient scripts in Tamil, which is one of the oldest languages in India. The approach utilizes Extreme Learning Machine for classification of handwritten ancient Tamil scripts. The Extreme Learning Machine is trained by Zernike moments and Regional features. The performance of Extreme Learning Machine is compared with Probabilistic Neural Networks. From the experimental results it is inferred that Extreme Learning Machine gives highest accuracy rate of 95%.

Keywords

handwritten character recognition; image classification; inference mechanisms; learning (artificial intelligence); natural language processing; India; Zernike moments; extreme learning machine training; handwritten Tamil character recognition; handwritten ancient Tamil script classification; inference; regional features; writing styles; Character recognition; Handwriting recognition; Neural networks; Optical character recognition software; Probabilistic logic; Support vector machine classification; Training; Classification; Extreme Learning Machine; Handwritten Tamil character; Probabilistic Neural Network; Zernike moments;

fLanguage

English

Publisher

ieee

Conference_Titel

Emerging Trends in Computing, Communication and Nanotechnology (ICE-CCN), 2013 International Conference on

Conference_Location

Tirunelveli

Print_ISBN

978-1-4673-5037-2

Type

conf

DOI

10.1109/ICE-CCN.2013.6528483

Filename

6528483