Vehicle Number Plate Recognition and Information Access System

Addressing the growing challenges posed by increasing vehicles and stringent traffic violations, many vehicle number plates go undetected while passing through toll booths and traffic lights. Thus it creates the need for a vehicle number plate recognition system. Existing systems employing ANPR algorithms utilize MATLAB and K-Nearest neighbour algorithms for image processing. Drawbacks include limitations in adapting to dynamic environments, Real time recognition, handling multiple vehicles, slow processing speeds, incapability of multiple object detection in single frame. The paper focuses on Automatic Number Plate Recognition (ANPR), employing specialized cameras and advanced image processing and using YOLOv8 for object detection and extracting license plate information from video feeds using EasyOCR. The noisy data generated during character recognition is filtered out using Regex based algorithm. The system integrates with centralized databases for real-time vehicle information access for users. The same database is used to automate e-challan updation, contributing to traffic management by swiftly issuing fines for rule violations. The proposed video-based processing system achieves number plate detection using YOLOv8 and storage of vehicle number plates in a centralized MongoDB database with an accuracy achieved of 96% in license plate detection and reduces noisy data using algorithm. It also enables real-time data retrieval through a user interface using Reactjs as frontend and is also connected to the MongoDB database for precise and user-friendly access in modern traffic systems.