Paper Contents
Abstract
Vehicle Number Plate Detection (VNPD) is a vital component of intelligent transportation systems and traffic monitoring solutions. This project focuses on the development of a system that automatically detects and extracts vehicle number plates from images using image processing techniques. The primary objective is to design a robust, real-time number plate detection system that can work under varying environmental conditions such as lighting, angle, and plate orientation.The system employs a sequence of steps starting with image acquisition followed by pre-processing techniques such as grayscale conversion, noise reduction, and contrast enhancement. Edge detection and morphological operations are then used to identify the region of interest (ROI) that corresponds to the license plate. Finally, character segmentation and Optical Character Recognition (OCR) are applied to extract and interpret the alphanumeric characters from the detected plate.This project uses OpenCV and Python for implementation, leveraging libraries such as Tesseract for OCR. The system is designed to be scalable and applicable for use in applications like automatic toll collection, parking management, and law enforcement. The experimental results demonstrate a high accuracy of detection and recognition, showing promise for real-world deployment.
Copyright
Copyright © 2025 Admanker Prem Raj. This is an open access article distributed under the Creative Commons Attribution License.
