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Automatic License Plate Recognition in Kurdistan Region of Iraq (KRI)

Abbas Mohamed Ali1, Shareef Maulod Shareef1 & Tarik Ahmed Rashid1

1Software Engineering Department, College of Engineering, Salahadin University

The development of countries increases the number of vehicles on the roads now than
there used to be. Consequently, controlling and managing the congestion of traffic is
virtually difficult without the use of computer technology. This paper aims to identify
automatic license plate recognition (ALPR) of vehicles in Kurdistan Region of Iraq
(KRI). It uses computer vision techniques where a cluster of Gabor feature vectors
using K-means is used, furthermore, the resulted cluster feature is optimized with
Wrapper Sub Eval technique to reduce the dimensionality of features vectors, then, the
optimized features are fed into classification techniques such as Support Vector
Machines (SVMs), K-Nearest neighbors (K-NN) and Radial Basis Function (RBF)
Neural Network in order to examine the recognition rate of the license plate of the
vehicle automatically. The experimental work shows that the proposed technique
produced promising classification results in recognizing license plate of vehicles. The
best optimal accuracy result under various illumination conditions was 96.72

Key Words: E-Government, Gabor Feature Vectors, License Plate Recognition, SVM.


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