Automatic Number Plate Recognition Using YOLOv8 Model

Abstract

Automatic Number Plate Recognition (ANPR) systems have become a critical tool in various sectors, including traffic management, law enforcement, and tolling systems. This paper presents an in-depth exploration of an advanced ANPR framework that leverages cutting-edge image processing methodologies and machine learning models to deliver exceptional accuracy in license plate detection and recognition. The system follows a multi-phase approach encompassing image capture, preprocessing, plate localization, character segmentation, and optical character recognition (OCR). Notably, the integration of YOLOv8, a state-of-the-art deep learning model for object detection, significantly enhances the feature extraction and classification process, boosting the system's performance across diverse environmental challenges. The proposed approach achieves a recognition accuracy exceeding 95%, highlighting its potential for deployment in real-world scenarios. Additionally, the paper addresses various challenges encountered in ANPR systems, such as variations in license plate formats, fluctuating lighting conditions, and partial occlusions, and proposes future research directions aimed at further improving robustness and operational efficiency.