Genetic algorithm based neural network for license plate recognition

Authors:
Wang Xiaobin;Li Hao;Wu Lijuan;Hong Qu
Affiliations:
Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, China;Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, China;Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, China;Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, China
Venue:
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
Year:
2013

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Abstract

This paper combines genetic algorithms and neural networks to recognize vehicle license plate characters. We train the neural networks using a genetic algorithm to find optimal weights and thresholds. The traditional genetic algorithm is improved by using a real number encoding method to enhance the networks weight and threshold accuracy. At the same time, we use a variety of crossover operations in parallel, which broadens the range of the species and helps the search for the global optimal solution. An adaptive mutation rate both ensures the diversity of the species and makes the algorithm convergence more rapidly to the global optimum. Experiments show that this method greatly improves learning efficiency and convergence speed.