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persian-license-plate-recognition

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truthofmatthew/persian-license-plate-recognition

PLPR utilizes YOLOv5 and custom models for high-accuracy Persian license plate recognition, featuring real-time processing and an intuitive interface in an open-source framework.

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Python GPL-3.0Created Feb 20, 2024

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Overview

PLPR utilizes YOLOv5 and custom models for high-accuracy Persian license plate recognition, featuring real-time processing and an intuitive interface in an open-source framework.

Capability facts

Languages
python

Source: github.language · Jul 11, 2026

Categories

Computer VisionInference & ServingLLM Frameworks

Compatibility

Sourced claims from the README excerpt - not unsourced marketing copy.

Python runtimePython

Source: README excerpt (regex_v1, Jul 11, 2026)

2. Install the required Python packages:
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Tags

aicomputer-visionimage processinglicense-plate-recognitionmachine-learningpersian-license-platepythonvehicle-identification

README

🚗 Persian License Plate Recognition System (PLPR)

The Persian License Plate Recognition (PLPR) system is a state-of-the-art solution designed for detecting and recognizing Persian license plates in images and video streams. Leveraging advanced deep learning models and a user-friendly interface, it ensures reliable performance across different scenarios.

💻 System Hardware Requirements

To ensure optimal performance of the Persian License Plate Recognition System (PLPR), the following hardware specifications are recommended:

  • Processor: Intel Core i5 (8th Gen) or equivalent/higher.
  • Memory: 8 GB RAM or more.
  • Graphics: Dedicated GPU (NVIDIA GTX 1060 or equivalent) with at least 4 GB VRAM for efficient real-time processing and deep learning model computations.
  • Storage: SSD with at least 20 GB of free space for software, models, and datasets.
  • Operating System: Compatible with Windows 10/11, Linux (Ubuntu 18.04 or later), and macOS (10.14 Mojave or later).

These specifications are designed to handle the computational demands of advanced deep learning models, real-time video processing, and high-volume data management integral to the PLPR system. Adjustments may be necessary based on specific deployment scenarios and performance expectations.

🔧 Installation

  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 required Python packages: 
    pip install -r requirements.txt

📄 License

GPL-3.0. See the LICENSE file for details. It means you can:

  • Share Source Code: If you distribute binaries or modified versions, you must make the source code available under GPL-3.
  • License: Must keep and apply GPL-3 to the modified work.
  • State Modifications: If modified, must disclose that it was changed.