Licence Plate Character Recognition Using Artificial Immune Technique

Authors:
Rentian Huang;Hissam Tawfik;Atulya Nagar
Affiliations:
Intelligent and Distributed Systems Lab, Deanery of Business and Computer Sciences, Liverpool Hope University, Liverpool, United Kingdom L16 9JD;Intelligent and Distributed Systems Lab, Deanery of Business and Computer Sciences, Liverpool Hope University, Liverpool, United Kingdom L16 9JD;Intelligent and Distributed Systems Lab, Deanery of Business and Computer Sciences, Liverpool Hope University, Liverpool, United Kingdom L16 9JD
Venue:
ICCS '08 Proceedings of the 8th international conference on Computational Science, Part I
Year:
2008

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Abstract

This paper proposes the application of Artificial Immune Technique in Licence Plate Character Recognition (LPCR). The use of Clonal Selection Algorithm (CSA) is composed of two main stages: (1) dynamic training samples; and (2) a choice of the best antibodies based on the three main clonal operations of cloning, clonal mutation and clonal selection. Once memory cells are established it will output the classification results using Fuzzy K-Nearest Neighbor (KNN) approach. The performance of CSA is compared to the Back Propagation Neural Networks (BPNN) in solving a LPCR problem. The experimental results show that the Artificial Immune Technique has a favorable performance in terms of being more accurate and robust.