Automated Vehicle Number Plate Recognition System for Toll and Parking Management

Objective: To design and implement a reliable automated system for recognizing vehicle number plates to streamline toll collection and parking access.

Manual toll collection and parking access in India cause traffic congestion, delays, and revenue leakage, affecting commuters, facility operators, and administrative authorities. Accurate vehicle identification is essential for efficient and transparent operations, but current systems are labor-intensive and prone to errors.

This project proposes an automated vehicle number plate recognition (ANPR) system that integrates AI-based image processing with IoT-enabled hardware at entry points of toll booths and parking lots. High-definition cameras capture vehicle images, which are processed using advanced OCR algorithms to extract and validate plate numbers against centralized databases for automated gate operations and billing.

Key deliverables include a working prototype comprising a camera module, embedded edge processor (e.g., Raspberry Pi), trained computer vision models (YOLO/CRNN), integration with a real-time database, and an automated barrier control system. The system will be demonstrated in real-time with live video feeds, vehicle detection, number plate recognition, and gate actuation.

Industry impact: The solution enhances throughput, minimizes human intervention, and reduces fraud at tolls and parking facilities. Scalable for smart city deployments, it is adaptable for urban infrastructure, highways, offices, malls, and residential complexes across India.