A Vehicle License Plate Detection and Recognition System

Abstract

Problem statement: Automatic vehicle license plate detection and recognition is a key technique in most of traffic related applications and is an active research topic in the image processing domain. Different methods, techniques and algorithms have been developed for license plate detection and recognitions. Approach: Due to the varying characteristics of the license plate from country to country like numbering system, colors, language of characters, style (font) and sizes of license plate, further research is still needed in this area. Results: In most of the middle East countries, they use the combination of Arabic and English letters, plus their countries logo. Thus, it makes the localization of plate number, the differentiation between Arabic and English letters and logo’s object and finally the recognition of those characters become more challenging research task. The use of artificial neural network has proved itself beneficial for plate recognition, but it has not been applied for the plate detection. Radial Basis Function (RBF) neural network is used both for the detection and recognition of Saudi Arabian license plate. Conclusion/Recommendations: The proposed approach has been tested on 200 front images of national license plate of Saudi Arabia. A higher percentage of accuracy has been obtained to show that the significant of this approach. The study could be further investigated on other middle east countries.