This AI-powered Number Plate Recognition System uses Python and OpenCV to detect and extract number plate information from both video files and live webcam feeds. It features real-time processing, suspicious plate detection with alerts, and automatic data storage. The system is designed for security applications, enhancing surveillance and vehicle monitoring efficiency.
The AI-Powered Number Plate Recognition System is a web-based application that utilizes Python, OpenCV, Flask, and OCR to accurately detect, recognize, and process vehicle number plates from video files and live webcam streams. The system operates in two modes: Video Mode, where users can upload a video file for processing, and Camera Mode, which enables real-time detection using a webcam.
Key features of this system include:
Real-time Number Plate Detection: Uses OpenCV and Haar cascades to detect number plates from video frames.
OCR-Based Plate Recognition: Extracts text from detected number plates using OCR (Optical Character Recognition).
Suspicious Vehicle Alert System: Predefined suspicious number plates trigger alerts, and detected images are saved for future reference.
Data Storage & Logging: Automatically saves recognized number plates in a CSV file for tracking and analysis.
Interactive Web Interface: Built using Flask, allowing users to upload videos, switch between modes, and view alerts in real time.
Efficient Processing & Security Enhancement: Ideal for surveillance, parking management, and security applications.
This project is a practical solution for law enforcement, security agencies, and smart surveillance systems, improving the efficiency of vehicle tracking and monitoring.
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