TITLE:
Neural Network-Powered License Plate Recognition System Design
AUTHORS:
Sakib Hasan, Md Nagib Mahfuz Sunny, Abdullah Al Nahian, Mohammad Yasin
KEYWORDS:
Intelligent Traffic Control Systems, Automatic License Plate Recognition (ALPR), Neural Networks, Vehicle Surveillance, Traffic Management, License Plate Recognition Algorithms, Image Extraction, Character Segmentation, Character Recognition, Low-Light Environments, Inclement Weather, Empirical Findings, Algorithm Accuracy, Simulation Outcomes, Digitalization
JOURNAL NAME:
Engineering,
Vol.16 No.9,
September
24,
2024
ABSTRACT: The development of scientific inquiry and research has yielded numerous benefits in the realm of intelligent traffic control systems, particularly in the realm of automatic license plate recognition for vehicles. The design of license plate recognition algorithms has undergone digitalization through the utilization of neural networks. In contemporary times, there is a growing demand for vehicle surveillance due to the need for efficient vehicle processing and traffic management. The design, development, and implementation of a license plate recognition system hold significant social, economic, and academic importance. The study aims to present contemporary methodologies and empirical findings pertaining to automated license plate recognition. The primary focus of the automatic license plate recognition algorithm was on image extraction, character segmentation, and recognition. The task of character segmentation has been identified as the most challenging function based on my observations. The license plate recognition project that we designed demonstrated the effectiveness of this method across various observed conditions. Particularly in low-light environments, such as during periods of limited illumination or inclement weather characterized by precipitation. The method has been subjected to testing using a sample size of fifty images, resulting in a 100% accuracy rate. The findings of this study demonstrate the project’s ability to effectively determine the optimal outcomes of simulations.