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A Comparative Study between the K-Nearest Neighbors and the Multi-Layer Perceptron for Cursive Handwritten Arabic Numerals Recognition

by B. El Kessab, C. Daoui, B. Boukhalene, R. Salouan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 107 - Number 21
Year of Publication: 2014
Authors: B. El Kessab, C. Daoui, B. Boukhalene, R. Salouan
10.5120/19140-0117

B. El Kessab, C. Daoui, B. Boukhalene, R. Salouan . A Comparative Study between the K-Nearest Neighbors and the Multi-Layer Perceptron for Cursive Handwritten Arabic Numerals Recognition. International Journal of Computer Applications. 107, 21 ( December 2014), 25-30. DOI=10.5120/19140-0117

@article{ 10.5120/19140-0117,
author = { B. El Kessab, C. Daoui, B. Boukhalene, R. Salouan },
title = { A Comparative Study between the K-Nearest Neighbors and the Multi-Layer Perceptron for Cursive Handwritten Arabic Numerals Recognition },
journal = { International Journal of Computer Applications },
issue_date = { December 2014 },
volume = { 107 },
number = { 21 },
month = { December },
year = { 2014 },
issn = { 0975-8887 },
pages = { 25-30 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume107/number21/19140-0117/ },
doi = { 10.5120/19140-0117 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:41:40.820335+05:30
%A B. El Kessab
%A C. Daoui
%A B. Boukhalene
%A R. Salouan
%T A Comparative Study between the K-Nearest Neighbors and the Multi-Layer Perceptron for Cursive Handwritten Arabic Numerals Recognition
%J International Journal of Computer Applications
%@ 0975-8887
%V 107
%N 21
%P 25-30
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper we present a comparison between two supervised classifiers, the first one is a statistic which is the K-Nearest Neighbors (KNN) while the second is a neuronal which is the multi-layer perceptron MLP in the recognition of cursive handwritten Arabic numerals. The recognition process is organized as follows: in the pre-processing of numeral images, we exploited the median filter, the thresholding, the centering and the normalization techniques, in the features extraction we have used the morphology mathematical method. The classification methods include the KNN and the MLP. The simulation results that we obtained demonstrate the MLP is more efficient than the KNN in this recognition.

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Index Terms

Computer Science
Information Sciences

Keywords

The cursive handwritten Arabic numerals: The median filter the thresholding the centering and the normalization techniques the mathematical morphology method the K-Nearest Neighbors (KNN) The multi-layer perceptron (MLP).

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