License Plate Recognition system is one kind of a traffic monitoring systems and image-processing technology used to identify vehicles by their Iraqi license plates. This technology is used for many applications such as finding stolen cars, parking decks, border control, and solution to the problem of monitoring the tremendous number of vehicles for law enforcement, various security and traffic applications. A little work has been done for Iraqi license plate recognition systems. Our study proposes new architecture in License Plate Character Recognition (LPCR) based on Support Vector Machines (SVMs) with Bee Colony Optimization Algorithm (BCOA). This Algorithm is used to dynamically select the best training data set for SVMs throughout training. The experimental results show that SVMs reduce the size of training dataset and training time, with the parameters optimization by BCOA. Our proposed Architecture has a satisfactory performance in terms of being more accurate and strong.
Keywords: Bee Colony Optimization Algorithm BCOA, Support Vector Machines SVMs, Vehicle license plate recognition
