Persian License Plate Recognition: Advanced Deep Learning Tool

Project Overview

Introduction

The Persian License Plate Recognition (PLPR) system is a sophisticated tool designed to detect and recognize Persian license plates in images and video streams. It employs advanced deep learning models, such as YOLOv5, for accurate detection and custom-trained models for precise recognition of Persian characters. The system is capable of real-time processing of live video feeds and features a user-friendly graphical user interface. It is particularly useful for applications in traffic monitoring and automated vehicle identification, addressing the unique challenges associated with Persian license plate recognition with high accuracy and efficiency.

Key Features

The Persian License Plate Recognition (PLPR) system is designed to detect and recognize Persian license plates in images and video streams with high accuracy and efficiency.

Key Features

• Advanced Detection: Utilizes YOLOv5 models for accurate license plate detection.
• Persian Character Recognition: Custom-trained models for precise recognition of Persian characters.
• Real-Time Processing: Capable of processing live video feeds in real-time.
• User-Friendly GUI: Intuitive graphical user interface for easy interaction.

Main Capabilities

• Detection and Recognition: Identifies license plates, recognizes Persian characters, and displays the recognized text.
• Resident Management: Manages resident vehicle information, permissions, and tracks vehicle movements.
• Entrance Management: Regulates vehicle entry and exit, maintaining a log of all vehicle movements.
• GUI Components: Displays input feed, detected plates, captured plate images, recognized text, owner name, plate status, and recent entries.

System Requirements

• Processor: Intel Core i5 (8th Gen) or equivalent.
• Memory: 8 GB RAM or more.
• Graphics: Dedicated GPU (NVIDIA GTX 1060 or equivalent) with at least 4 GB VRAM.
• Storage: SSD with at least 20 GB of free space.
• Operating System: Compatible with Windows 10/11, Linux (Ubuntu 18.04 or later), and macOS (10.14 Mojave or later).

The PLPR system is well-suited for applications in traffic monitoring, automated vehicle identification, and similar fields, offering a reliable and efficient solution for recognizing Persian license plates.

Real-World Applications

Traffic Monitoring

• Automated Vehicle Tracking: Use the PLPR system to monitor and track vehicles in real-time, enhancing traffic management and surveillance.
• Violation Detection: Automatically detect and log vehicles with expired or invalid licenses, aiding in enforcement of traffic regulations.

Automated Vehicle Identification

• Parking Management: Implement the system in parking facilities to automatically identify and manage vehicle entries and exits.
• Access Control: Ensure only authorized vehicles enter secured premises by integrating the PLPR system with access control systems.

Resident Management

• Vehicle Registration: Maintain a database of resident vehicles, managing permissions and tracking movements within residential areas.
• Security Logging: Keep a log of all vehicle movements for security and administrative purposes.

Entrance Management

• Authorized Entry: Regulate vehicle entry and exit points, ensuring only authorized vehicles gain access to premises.
• Real-Time Alerts: Set up the system to send real-time alerts for unauthorized or non-registered vehicles attempting to enter.

Exploring and Benefiting from the Repository

Installation and Setup

• Clone the repository and follow the installation instructions to set up the system on your local machine.

git clone https://github.com/mtkarimi/smart-resident-guard.git
cd smart-resident-guard
pip install -r requirements.txt

Customizing Video Sources

• Configure the video source to use either a webcam, video file, or real-time stream by modifying the config.ini file.

python home-yolo.py

Using the GUI

• Interact with the user-friendly GUI to upload images or video streams, view detected license plates, and recognize text.
• Adjust parameters to optimize performance based on your specific needs.

Learning More

• Refer to the detailed Wiki for a deep dive into the system’s architecture, model training, and advanced usage.
• Explore additional academic resources in the pdf-research directory for insights into LPR technologies.

By leveraging these features and examples, users can effectively utilize the Persian License Plate Recognition system for various applications, enhancing efficiency and accuracy in vehicle identification and management.

Conclusion

Key Points:

• Advanced Detection: Utilizes YOLOv5 for high-accuracy license plate detection.
• Real-Time Processing: Capable of processing live video feeds in real-time.
• User-Friendly GUI: Intuitive interface for easy interaction.
• Resident and Entrance Management: Manages vehicle information, permissions, and tracking.

Impact:

• Enhances traffic monitoring and automated vehicle identification.
• Improves security and administrative efficiency in managing vehicle movements.

Future Potential:

• Integration in various applications such as smart cities, parking systems, and border control.
• Potential for expansion to recognize other types of plates and characters.
• Continuous improvement through updates and contributions from the open-source community.

Hardware and Software Readiness:

• Optimized for performance with recommended hardware specifications.
• Compatible with multiple operating systems and configurable for different video sources.

For further insights and to explore the project further, check out the original truthofmatthew/persian-license-plate-recognition repository.

Attributions

Content derived from the truthofmatthew/persian-license-plate-recognition repository on GitHub. Original materials are licensed under their respective terms.