Integrated YOLOv8-Based Automatic Vehicle Number Plate Detection and Recognition System
Keywords:
Automatic Number Plate Recognition, ANPR, YOLOv8, PaddleOCR, OpenCV, Deep Learning, Optical Character Recognition, Vehicle Detection, Flask, SQLiteAbstract
Automatic Number Plate Recognition (ANPR) is an important computer vision technology used for automatic identification of vehicles through their license plates. Traditional vehicle identification methods require manual monitoring and are time consuming when a large number of vehicles need to be processed. Recent developments in deep learning and optical character recognition have enabled automated systems capable of detecting and recognizing vehicle number plates from images and live video streams. This paper presents a web-based Vehicle Number Plate Detection and Recognition System that combines YOLOv8,
OpenCV, PaddleOCR, Flask, and SQLite. The YOLOv8 model is used to detect and localize the vehicle number plate from an input image or live camera stream. After detection, the plate region is cropped and processed using PaddleOCR to recognize the characters present on the plate. OpenCV is used for image and video processing, while SQLite is used to store recognized vehicle information and maintain detection history. The proposed system provides separate modules for image upload, live detection, dashboard visualization, and history management. The integration of object detection and optical character recognition provides an automated approach for vehicle identification and can be applied to traffic monitoring, parking management, security systems, and intelligent transportation applications.
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