Iranian License Plate Character Recognition Using Mixture of MLP Experts

This paper presents a new classification framework for Iranian license plate character recognition. In this framework, a set of robust features are calculated from license plate characters based on directional projections and Kirsch edge detector, and then classified using mixture of experts which uses the multilayer perceptrons (MLPs) as expert and gating networks. The proposed recognition algorithm is evaluated on a database of Iranian license plate characters consisting of about 12000 binary images, and the recognition rate of 99.68% is achieved. Experimental results show that the proposed algorithm yields better performance of Iranian license plate character recognition in comparison with conventional methods which use a single MLP neural network.