A memetic-based fuzzy support vector machine model and its application to license plate recognition

In this paper, a novel fuzzy support vector machine (FSVM) coupled with a memetic particle swarm optimization (MPSO) algorithm is introduced. Its application to a license plate recognition problem is studied comprehensively. The proposed recognition model comprises linear FSVM classifiers which are used to locate a two-character window of the license plate. A new MPSO algorithm which consists of three layers i.e. a global optimization layer, a component optimization layer, and a local optimization layer is constructed. During the construction process, MPSO performs FSVM parameters tuning, feature selection, and training instance selection simultaneously. A total of 220 real Malaysian car plate images are used for evaluation. The experimental results indicate the effectiveness of the proposed model for undertaking license plate recognition problems.