Abstract:
This paper introduces an Automatic License Plate Detection (ALPD) system, incorporating
You Only Look Once version 8 (YOLO v8) for real-time object detection and OpenCV for
efficient image processing. With a focus on achieving accurate and swift license plate
recognition, the system employs YOLO v8 for precise localization within images or video
streams, complemented by OpenCV for effective preprocessing tasks. The Optical Character
Recognition (OCR) component of the system adopts a collaborative approach, leveraging Easy
OCR. This ensemble enhances the system's proficiency in accurately extracting alphanumeric
characters from license plates, even in challenging conditions like variable lighting and
complex backgrounds. Beyond license plate identification, the system emphasizes the
extraction of relevant textual information, making it versatile for applications in law
enforcement, traffic management, and security. Through The model were evaluated based on
their accuracy, precision recall. YOLOv8 highest accuracy 97%. This research highlights the
important of integrating advance deep learning technique. detailed implementation insights and
experimental results, this paper showcases the effectiveness of combining YOLO v8 and
OpenCV for object detection, as well as the collaborative strength of Easy OCR for precise
character recognition. The experimental results underscore the system's high accuracy and realtime performance, positioning it as a reliable tool for intelligent transportation systems and
security applications. This ALPD system offers a robust solution for diverse use cases,
contributing to advancements in efficient license plate recognition technology
