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Original: 26 Apr. 2015 Revised: 10 June 2015 Accepted: 25 June 2015 Published online: 20 Sep. 2015 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

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