Using sliding concentric windows for license plate segmentation and processing

Abstract

In this paper, a new algorithm for vehicle license plate identification is proposed, on the basis of a novel adaptive image segmentation technique (sliding windows) in conjunction with a character recognition neural network. The algorithm was tested with 2820 natural scene gray level vehicle images of different backgrounds and ambient illumination. The camera focused on the plate, while the angle of view and the distance from the vehicle varied according to the experimental setup. The license plates properly segmented were 2719 over 2820 input images (96.4%). The optical character recognition (OCR) system is a two layer probabilistic neural network with topology 108-180-36, whose performance reached 97.4%. The PNN was trained to identify multi-font alphanumeric characters from car license plates based on data obtained from algorithmic image processing.