Number Recognition in the Saudi License Plates using Classification and Clustering Methods

Authors

Salah Al-Shami, Department of Mathematics and Computer Science, Beirut Arab University, Debbeye, Lebanon.Follow
Ali El-Zaart, Department of Mathematics and Computer Science, Beirut Arab University, Debbeye, Lebanon.Follow
Ahmed Zekri, Department of Mathematics and Computer Science, Beirut Arab University, Debbeye, Lebanon.\\ Deptartment of Mathematics and Computer Science, Alexandria University, Alexandria, Egypt.Follow
Khaled Almustafa, College of Engineering, Prince Sultan University, Riyadh, KSA.Follow
Rached Zantout, Electrical and Computer Engineering, Rafik Hariri University, Meshref, Lebanon.Follow

Author Country (or Countries)

Lebanon.

Abstract

This study is conducted for the purpose of recognizing numbers printed on the license plates images. The size of different patterns in the Saudi license plates is 20: the Arabic and English numbers. It is hard to classify all the numbers due to the similarity between specific numbers. This paper will propose to use a clustering technique called X-Means in order to regroup the numbers that have the same characteristics. Later develop a specific classification technique for each cluster. The experimentation of the proposed approach is applied on our constructed dataset gave us some limitation in classification. The results are improved by constructing a reference image for each class selected using a specific criteria from the training dataset. Moreover, the experimental results ensure better recognition accuracy by using the proposed methods rather than classifying the same dataset using other classical classifier in the state-of-art.

Digital Object Identifier (DOI)

http://dx.doi.org/10.18576/amis/110115

Recommended Citation

Al-Shami, Salah; El-Zaart, Ali; Zekri, Ahmed; Almustafa, Khaled; and Zantout, Rached (2017) "Number Recognition in the Saudi License Plates using Classification and Clustering Methods," Applied Mathematics & Information Sciences: Vol. 11: Iss. 1, Article 15.
DOI: http://dx.doi.org/10.18576/amis/110115
Available at: https://digitalcommons.aaru.edu.jo/amis/vol11/iss1/15