A Statistical Learning Model for Number Plate Recognition for Vehicular Security

Authors

  • C. G. Udomboso Department of Statistics, University of Ibadan, Nigeria
  • J. D Akinyemi Department of Computer Science, University of York, United Kingdom
  • E. S. Irmiya Department of Computer Science, University of Ibadan, Nigeria
  • K. T. Ladoja Department of Computer Science, University of Ibadan, Nigeria
  • I . E Akinseye Department of Biochemistry, University of Ibadan, Nigeria
  • A .C. Omoyajowo Department of Statistics, Bowen University, Iwo, Nigeria

Keywords:

Computer Vision, Statistical image,, License Plate Recognition, YOLOv5, Tesseract OCR Engine

Abstract

Conventional security protocols at organizational gates that depend on human monitoring of vehicle traffic frequently fall short because of data inconsistencies and errors. This research makes use of computer vision techniques to suggest a statistical image processing system for tracking vehicle movements within businesses. It focuses specifically on the University of Ibadan in Nigeria's innovative Vehicle License Plate Recognition (VLPR) system for tracking automobiles. The Tesseract OCR engine and YOLOv5 were utilized by the system to attain 89% detection accuracy and 93% recognition accuracy. This resulted in a reliable solution that can improve security, traffic monitoring, and decision-making. The present study addresses a significant void in the Nigerian setting by providing a beneficial framework for the effective surveillance and management of vehicles.

Downloads

Published

2024-08-07

Issue

Vol. 12 No. 1 (2024): Journal of Science and Logics in ICT Research

Section

Articles