An Efficient Method of Vehicle License Plate Recognition Based on Sliding Concentric Windows and Artificial Neural Network

Abstract

This paper describes a new approach to analyze road images which often contain vehicles and extract license plate (LP) from natural properties by finding vertical and horizontal edges. In this paper, initially, segmentation technique named as sliding concentric windows (SCW) on the basis of a novel adaptive image segmentation technique for detecting candidate region. Color verification for candidate region by using HSI color model on the basis of using hue and intensity in HSI color model verifying green and yellow LP and white LP, respectively. Tilt correction by the least square fitting with perpendicular offsets (LSFPO) is proposed and implemented for estimating rotation angle of the LP region. Then the whole image is rotated for correcting tilt by this angle. Finally, a new algorithm based on artificial neural network (ANN) is used for recognition of Korean plate characters. Various LP images are used with a variety of conditions to test the proposed method and results are presented to prove its effectiveness.