Automatic Number Plate Recognition using YOLOv11

Abstract— This research presents a robust and efficient system for Automatic Number Plate Detection and Recognition using the latest YOLO v11 object detection algorithm and the COCO pre-trained model. Designed for real-time applications, the system automates the process of identifying vehicle license plates and accurately extracting their alphanumeric content using PaddleOCR. The implementation integrates a comprehensive tech stack including Python, OpenCV, Ultralytics, PaddlePaddle, and a MySQL database hosted via XAMPP for seamless data storage and retrieval. The system is trained on a custom dataset enhanced with COCO images, fine-tuned to accurately detect license plates in diverse environmental conditions. This solution aims to improve the efficiency of traffic monitoring, law enforcement, toll collection, and parking management by offering scalable, high-performance license plate recognition with minimal human intervention.

Keywords— Automatic Number Plate Recognition (ANPR), YOLO v11, COCO Dataset, PaddleOCR, Real-Time Object Detection, Deep Learning, License Plate Detection, Computer Vision, Traffic Surveillance, Vehicle Monitoring, OpenCV, PaddlePaddle, OCR, Ultralytics, Intelligent Transportation System (ITS).