A comparison of supervised classification methods for a statistical set of features: Application: Amazigh OCR

Author

Aharrane, Nabil ; El Moutaouakil, Karim ; Satori, Khalid

Author_Institution

Comput. sci., Imaging & Numerical Anal. Lab. (LIIAN), USMBA Univ., Fez, Morocco

fYear

2015

fDate

25-26 March 2015

Firstpage

1

Lastpage

8

Abstract

This paper is devoted to the study of supervised learning methods as part of pattern recognition and especially the Amazigh Characters Recognition. The goal is to compare a partial list of the popular automatic classification methods, and test the performance of the proposed features set extracted from isolated characters using statistical methods with these different classifiers. In Experimental evaluation, several runs have been conducted for the different algorithms and the best accuracy observed is for the multilayer perceptron with a recognition rate about 96,47% which is very satisfactory.

Keywords

character recognition; feature extraction; image classification; multilayer perceptrons; set theory; statistical analysis; Amazigh character recognition; automatic classification methods; isolated characters; multilayer perceptron; pattern recognition; statistical feature set extraction; statistical methods; supervised classification methods; supervised learning methods; Bayes methods; Classification algorithms; Decision trees; Feature extraction; Histograms; Image segmentation; Optical character recognition software; Amazigh; Machine learning; OCR; features extraction; intelligent data analysis; supervised classification;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Systems and Computer Vision (ISCV), 2015

Conference_Location

Fez

Print_ISBN

978-1-4799-7510-5

Type

conf

DOI

10.1109/ISACV.2015.7106171

Filename

7106171