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Automatic License Plate Recognition System
Advanced ALPR system using YOLO and OCR for real-time vehicle identification and traffic monitoring.
4 months
Lead ML Engineer
Computer Vision
Project Overview
This project implements a comprehensive Automatic License Plate Recognition (ALPR) system that combines state-of-the-art computer vision techniques with optical character recognition to identify and track vehicles in real-time. The system is designed to work in various lighting conditions and can handle multiple vehicle types.
Key Features
- •Real-time license plate detection using YOLO v5
- •Optical Character Recognition with Tesseract
- •Multi-angle plate recognition
- •Vehicle tracking and counting
- •Database integration for vehicle records
- •Web-based dashboard for monitoring
- •API for third-party integrations
Technical Challenges
- •Handling various lighting conditions and weather
- •Recognizing plates from different angles
- •Optimizing for real-time performance
- •Dealing with motion blur and low-resolution images
- •Supporting multiple license plate formats
Results & Impact
- •95% accuracy in license plate detection
- •Real-time processing at 30 FPS
- •Successfully deployed in 5+ locations
- •Reduced manual monitoring by 80%
- •Improved traffic law enforcement efficiency
Technologies Used
PythonYOLO v5OpenCVTensorFlowTesseract OCRNumPyPandas
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