The Persian License Plate Recognition (PLPR) system is a cutting-edge solution designed to recognize and detect Persian license plates from images and video feeds. This guide will walk you through setting up and using the PLPR system while providing some troubleshooting tips to enhance your experience.

Overview of the PLPR System

Built to address the unique challenges posed by Persian license plates, this system offers high accuracy and efficiency. Applications include traffic monitoring, automated vehicle identification, and more. With advanced features, it utilizes deep learning models and an intuitive user interface for reliable performance.

Key Features

• Advanced Detection: Uses YOLOv5 models for precise license plate detection.
• Persian Character Recognition: Employs custom-trained models for accurate recognition of Persian characters.
• Real-Time Processing: Processes live video feeds instantly.
• User-Friendly GUI: Simplifies interactions with a straightforward graphical user interface.

Main GUI Explanation

1 Input View: Displays video or camera feed. 2 Detected Plate Highlight: Rectangular overlay around detected plates. 3 Plate Image Display: Shows images of detected plates. 4 Extracted Text: Text recognized from the plate image. 5 Owner Name: Displays registered owner’s name. 6 Plate Status: Indicates if the plate is allowed, not authorized, or non-registered. 7 Recent Entries Table: Displays the last 10 entries with options to manage them.

System Hardware Requirements

To ensure the Persian License Plate Recognition System runs smoothly, you will need the following hardware specifications:

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

Getting Started

Installation

Follow these simple steps to set up the PLPR system:

1. Clone the repository and navigate to its directory:

git clone https://github.com/mtkarimi/smart-resident-guard.git
cd smart-resident-guard

2. Install the necessary Python packages:

pip install -r requirements.txt

Configuring Video Source

To customize the video source for your processing needs:

Modify the parameter in cv2.VideoCapture(0) for webcam input. To use a specific video file, update this parameter in config.ini:

video = anpr_video.mp4

For streaming videos, set the stream address in config.ini:

rtsp = rtsp://172.17.0.1:8554/webCamStream

Running the Application

To launch the application, use the command:

python home-yolo.py

Usage

The GUI allows users to upload images or video streams, displaying recognized license plates and their respective texts.

Troubleshooting

If you run into any issues while setting up or using the PLPR system, consider these troubleshooting steps:

• Ensure your hardware meets the system requirements.
• Double-check the path defined in config.ini for video sources.
• Make sure all required Python packages are installed correctly.
• Consult the community and documentation for additional guidance.
• For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

Conclusion

The Persian License Plate Recognition System exemplifies innovation in vehicle identification technologies. By implementing this system, users can significantly streamline vehicle tracking and monitoring processes.