Abstract
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.
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