A Hybrid Approach for Automatic Licence Plate Recognition System

Abstract

Background: Automatic licence plate recognition system is used for various applications such as traffic monitoring, toll collection, car parking, law enforcement. Objective: In this paper, a convolutional neural network and support vector machine-based licence plate recognition system is proposed. Method: Firstly, from the input image of the vehicle, the characters are extracted and segmented. Then features of the segmented characters are extracted. The extracted features are classified using convolutional neural networks and support vector machine for the final recognition of the licence plate. Results: The obtained recognition rate by the hybridization of the convolutional neural network and the support vector machine is 96.5%. The obtained results for the proposed hybrid automatic licence plate system is compared with three other automatic licence plate systems based on neural network, support vector machine, and convolutional neural network. Conclusion: The proposed hybrid ALPR system performs better than NN, SVM, and CNN based ALPR systems.