Enhancing ANPR accuracy for staff car verification using convolutional neural networks and RegEX correction

Abstract

This research paper presents the development and evaluation of an Automatic Number Plate Recognition (ANPR) system using Convolutional Neural Networks (CNN) with Regex correction. The aim is to enhance the accuracy and effectiveness of car verification and security processes at First Technical University, Ibadan. The ANPR system was implemented both without Regex correction and with Regex correction. The evaluation results demonstrate significant improvements in the system's performance when CNN with Regex correction is employed. The CNN-based ANPR system achieves a precision of 1.00, recall of 0.90, and F1-score of 0.95 in accurately identifying number plates. These scores indicate increased accuracy and reduce false positives compared to the system without Regex correction. The integration of CNN and Regex correction effectively handles variations and errors in the number plate data, leading to a reliable and efficient car verification process. Future work can focus on further refining the CNN model and optimizing the Regex correction algorithms to enhance the system's accuracy and robustness. The developed ANPR system, utilizing CNN with Regex correction, shows great potential for enhancing car verification and security in various domains, including law enforcement, parking management, and traffic monitoring