License Plate Character Recognition Using Mixture of Expert Architecture

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,kirsch edge detector and local means. The characters are then classified using mixture of experts which use 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 14256 binary images, and the recognition rate of 99.42% is achieved. The proposed algorithm yields better performance of the Iranian license plate character recognition in comparison with conventional methods which use a single MLP neural network.