ech T Press Science Computers, Materials & Continua DOI:10.32604/cmc.2021.014924 Article Automatic Vehicle License Plate Recognition Using Optimal Deep Learning Model Thavavel Vaiyapuri 1 , Sachi Nandan Mohanty 2 , M. Sivaram 3 , Irina V. Pustokhina 4 , Denis A. Pustokhin 5 and K. Shankar 6, * 1 College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia 2 Department of Computer Science & Engineering, IcfaiTech, ICFAI Foundation for Higher Education, Hyderabad, India 3 Assistant professor Research, Research Center, Lebanese French University, Erbil, 44001, Iraq 4 Department of Entrepreneurship and Logistics, Plekhanov Russian University of Economics, Moscow, 117997, Russia 5 Department of Logistics, State University of Management, Moscow, 109542, Russia 6 Department of Computer Applications, Alagappa University, Karaikudi, India * Corresponding Author: K. Shankar. Email: drkshankar@ieee.org Received: 27 October 2020; Accepted: 23 November 2020 Abstract: The latest advancements in highway research domain and increase in the number of vehicles everyday led to wider exposure and attention towards the development of effcient Intelligent Transportation System (ITS). One of the popular research areas i.e., Vehicle License Plate Recognition (VLPR) aims at determining the characters that exist in the license plate of the vehicles. The VLPR process is a diffcult one due to the differences in viewpoint, shapes, colors, patterns, and non-uniform illuminationat the time of capturing images. The current study develops a robust Deep Learning (DL)-based VLPR model using Squirrel Search Algorithm (SSA)-based Convolutional Neural Network (CNN), called the SSA-CNN model. The presented technique has a total of four major processes namely preprocessing, License Plate (LP) localization and detection, character segmentation, and recognition. Hough Transform (HT) is applied as a feature extractor and SSA-CNN algorithm is applied for character recognition in LP. The SSA-CNN method effectively recognizes the characters that exist in the segmented image by optimal tuning of CNN parameters. The HT-SSA-CNN model was experimentally validated using the Stanford Car, FZU Car, and HumAIn 2019 Challenge datasets. The experimentation outcome verifed that the presented method was better under several aspects. The projected HT-SSA-CNN model implied the best performance with optimal overall accuracy of 0.983%. Keywords: Deep learning; license plate recognition; intelligent transportation; segmentation 1 Introduction Vehicle License Plate Recognition (VLPR) has been a major computer vision issue in recent decades. In this scenario, the prevalent systems of cameras are placed at road junctions to fnd 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.