Automatic Vehicle Number Plate Recognition: Computer Engineering Guide
1. Introduction
Overview of the project.
Objectives of the system: Enable automatic detection and recognition of vehicle number plates for traffic management and law enforcement.
Scope of the system: Applications include toll collection, parking management, and surveillance.
2. Requirements Analysis
Functional Requirements:
· - Capture vehicle images using cameras.
· - Extract number plates from images.
· - Perform Optical Character Recognition (OCR) to identify plate characters.
· - Store recognized data and generate logs.
Non-Functional Requirements:
· - High accuracy in number plate recognition.
· - Fast processing to handle high vehicle flow.
· - Robust performance in varying environmental conditions (e.g., lighting, weather).
3. System Design
Architecture:
· - Image processing and OCR modules integrated with a central database.
· - Use of edge devices for real-time processing.
Data Flow Diagrams (DFDs):
· - Level 0: Overview of data flow from image capture to data storage.
· - Level 1: Modules like Image Capture, Plate Detection, OCR, and Data Storage.
Database Design:
· - Tables: Vehicle Records, Recognized Plates, Logs, Alerts.
4. Technology Stack
Hardware:
· - High-resolution cameras for image capture.
· - Edge devices like Raspberry Pi or Jetson Nano for real-time processing.
Software:
· - Image Processing: OpenCV or MATLAB.
· - OCR: Tesseract OCR or Google Vision API.
Backend Development:
· - Python (Flask/Django) or Java (Spring Boot) for APIs.
Database:
· - SQL (MySQL, PostgreSQL) or NoSQL (MongoDB) for storing vehicle records.
Communication Protocols:
· - Secure protocols for data transfer between devices and servers.
5. Implementation
Hardware Setup:
· - Install cameras at strategic locations for optimal image capture.
· - Connect cameras to processing units for real-time data transfer.
Software Development:
· - Develop modules for image preprocessing and plate detection.
· - Implement OCR algorithms for character recognition.
· - Create backend APIs for storing and retrieving vehicle data.
Integration:
· - Combine hardware and software components for seamless operation.
· - Implement fail-safe mechanisms for hardware or network failures.
6. Security
Encrypt data during transmission and storage.
Implement access control for database and system resources.
Regularly audit the system for potential vulnerabilities.
7. Testing
Unit Testing: Validate individual components like image processing and OCR.
Integration Testing: Ensure smooth interaction between cameras, processing units, and backend services.
System Testing: Test the system under various traffic conditions.
Performance Testing: Evaluate system accuracy and processing speed.
8. Deployment
Deploy cameras and processing units at designated locations.
Configure backend services on cloud platforms for scalability.
Train personnel on system usage and maintenance.
9. Maintenance and Updates
Regularly update OCR algorithms for improved accuracy.
Monitor system performance and address any issues promptly.
Collect user feedback and implement feature enhancements.
10. Appendix
Glossary of terms.
References and additional resources.