Enhancement of Vehicle Number Plate Recognition System

Abstract: Automatic Number Plate Recognition (ANPR) is an important computer vision technology used in Intelligent Transportation Systems (ITS) for traffic monitoring, law enforcement, parking management, toll collection, and vehicle access control. Previous ANPR systems have primarily relied on conventional image processing techniques, including thresholding, edge detection, morphological operations, and rule-based plate localization. Although these methods can perform effectively under controlled conditions, their accuracy is often affected by variations in illumination, vehicle orientation, background complexity, plate size, and image quality. To address these limitations, this proposed research presents an enhanced Automatic Number Plate Recognition system that integrates the YOLOv9(You Look Only Once Version 9) object detection algorithm with image processing and Optical Character Recognition (OCR) techniques. YOLOv9 is used to automatically detect and accurately localize vehicle number plates from input images, eliminating the need for predefined assumptions regarding the position and size of the plate. The detected plate region is then processed using grayscale conversion, noise reduction, histogram equalization, morphological operations, and Sobel edge detection to enhance the image and improve character separation. The segmented alphanumeric characters are subsequently recognized using an OCR module, and the extracted registration number can be matched with a database for vehicle information retrieval. By combining deep learning-based object detection (YOLO) with median filter and OCR, the proposed approach improves the robustness and reliability of the ANPR system under varying image conditions. To test the hypothesis, this research utilized the publicly available zenodo dataset for the vehicle images used to train, validate, and test the Automatic Number Plate Recognition (ANPR) system. It provides the information required for the YOLOv9 model to learn how to detect number plates and for the OCR module to recognize the characters correctly. It is a diverse dataset containing different vehicle types, lighting conditions, viewing angles, and backgrounds helps improve the accuracy and robustness of the proposed system. Experimental results demonstrate that the proposed system achieves 94.1% number plate detection accuracy and 97.2% Character recognition accuracy, indicating significant enhancement over previously developed ANPR techniques and demonstrating the effectiveness of the integrated YOLO- based ANPR framework for practical vehicle identification applications.

Keywords: Image Processing, Text Extraction, Character recognition, YOLOv9, ANPR, Number Plate Detection, MATLAB