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Hybrid Metaheuristics Based License Plate Character Recognition in Smart City

Esam A. AlQaralleh1, Fahad Aldhaban2, Halah Nasseif2, Bassam A.Y. Alqaralleh2,*, Tamer AbuKhalil3

1 School of Engineering, Princess Sumaya University for Technology, Amman, 11941, Jordan
2 MIS Department, College of Business Administration, University of Business and Technology, Jeddah, 21448, Saudi Arabia
3 Department of Computer Science, Faculty of Information Technology, Al-Hussein Bin Talal University, Ma'an, 71111, Jordan

* Corresponding Author: Bassam A.Y. Alqaralleh. Email: email

Computers, Materials & Continua 2022, 72(3), 5727-5740. https://doi.org/10.32604/cmc.2022.026780

Abstract

Recent technological advancements have been used to improve the quality of living in smart cities. At the same time, automated detection of vehicles can be utilized to reduce crime rate and improve public security. On the other hand, the automatic identification of vehicle license plate (LP) character becomes an essential process to recognize vehicles in real time scenarios, which can be achieved by the exploitation of optimal deep learning (DL) approaches. In this article, a novel hybrid metaheuristic optimization based deep learning model for automated license plate character recognition (HMODL-ALPCR) technique has been presented for smart city environments. The major intention of the HMODL-ALPCR technique is to detect LPs and recognize the characters that exist in them. For effective LP detection process, mask regional convolutional neural network (Mask-RCNN) model is applied and the Inception with Residual Network (ResNet)-v2 as the baseline network. In addition, hybrid sunflower optimization with butterfly optimization algorithm (HSFO-BOA) is utilized for the hyperparameter tuning of the Inception-ResNetv2 model. Finally, Tesseract based character recognition model is applied to effectively recognize the characters present in the LPs. The experimental result analysis of the HMODL-ALPCR technique takes place against the benchmark dataset and the experimental outcomes pointed out the improved efficacy of the HMODL-ALPCR technique over the recent methods.

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APA Style
AlQaralleh, E.A., Aldhaban, F., Nasseif, H., Alqaralleh, B.A., AbuKhalil, T. (2022). Hybrid metaheuristics based license plate character recognition in smart city. Computers, Materials & Continua, 72(3), 5727-5740. https://doi.org/10.32604/cmc.2022.026780
Vancouver Style
AlQaralleh EA, Aldhaban F, Nasseif H, Alqaralleh BA, AbuKhalil T. Hybrid metaheuristics based license plate character recognition in smart city. Comput Mater Contin. 2022;72(3):5727-5740 https://doi.org/10.32604/cmc.2022.026780
IEEE Style
E.A. AlQaralleh, F. Aldhaban, H. Nasseif, B.A. Alqaralleh, and T. AbuKhalil "Hybrid Metaheuristics Based License Plate Character Recognition in Smart City," Comput. Mater. Contin., vol. 72, no. 3, pp. 5727-5740. 2022. https://doi.org/10.32604/cmc.2022.026780



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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