Title: Convolutional neural networks and support vector machines for hybrid number plate recognition model

Authors: Peter Muthuri Kibaara; Edna C. Too; David Gitonga Mwathi

Addresses: Chuka University, 109-60400 Chuka, Kenya ' Department of Computer Science, Chuka University, 109-60400 Chuka, Kenya ' Department of Computer Science, Chuka University, 109-60400 Chuka, Kenya

Abstract: Recognition accuracy is a determinant performance metric factor that greatly affects the optimal implementation of ANPRs. The recognition accuracy of the existing ANPRs can be improved by adopting a hybrid approach that leverages on the strengths of two machine learning algorithms, i.e., CNN and SVM that have previously been deployed independently in ANPRs. Two models were developed using a deep cascade framework: a pure CNN with a SoftMax classifier and a hybrid CNN with a SVM classifier. UFPR-ALPR dataset was used to train validate and test the models. The hybrid CNN-SVM model had a recognition accuracy of 91.25% against 89.07% from the pure CNN model. The weighted average precision, recall, and F1-score of the hybrid CNN-SVM was 92%, 91% and 91%, respectively, which was better compared to that of pure CNN. The hybrid model was tested for external validity using the SSIG dataset. The hybrid CNN-SVM model had a recognition accuracy of 91% against 89% from the pure CNN model. The weighted average precision, recall, and F1-score of the hybrid CNN-SVM was 91%, 91% and 91% respectively which was better compared to that of pure CNN.

Keywords: hybrid; convolutional neural networks; support vector machines; automatic number plate recognition; ANPR; confusion matrix; accuracy; precision; recall; F1-score.

DOI: 10.1504/IJHI.2023.129322

International Journal of Hybrid Intelligence, 2023 Vol.2 No.2, pp.128 - 150

Received: 25 Feb 2022
Accepted: 24 May 2022
Published online: 06 Mar 2023 *

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