A New Hierarchical Architecture Based on SVM for Persian License Plate Character Recognition

Each license plate recognition system is composed of three main parts, namely, license plate detection, character segmentation and character recognition. In this paper, we focus on the improvement and innovation of the character recognition step. For this purpose, a new hierarchical architecture based on Support Vector Machines (SVMs) is suggested for Persian license plate characters recognition. Clustering characters in the hierarchical architecture is proposed based on a new criterion using a confusion matrix. The criterion and the confusion matrix have not been used for clustering in this way. The hierarchical architecture precision is 97/4% and recognizes the entire characters of a license plate in approximately 60ms. All evaluations and comparisons among the performances of previous methods and the proposed method in this paper have been done in the same hardware and software test bed. The dataset obtained from images in real conditions such as, day, night, different distances and angles.