Licence Plate Character Recognition Based on Support Vector Machines with Clonal Selection and Fish Swarm Algorithms

Abstract

This paper proposes a new hybrid approach in licence plate character recognition (LPCR) based on support vector machines (SVMs) with clonal selection and fish swarm algorithms. The artificial immune technique is used through clonal selection algorithm (CSA) to dynamically select the best training data set for SVMs throughout training. The artificial fish swarm algorithm (AFSA) is for parameters optimization which including C, delta <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ,epsiv, and for SVMs. This method has been applied in a car park monitoring system with comparison with back propagation neural networks (BPNN) and standard SVMs. The experimental results show that CSA helped SVMs reduce the size of training dataset and training time; with the parameters optimization by AFSA. Our new hybrid method has a favorable performance in terms of being more accurate and robust.