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Persian License Plate Recognition System

Persian License Plate Recognition System is a Python desktop application that detects Persian license plates in images and video, recognizes their characters with custom-trained models, and presents the results in a graphical interface. It uses YOLOv5 for plate detection and describes database-backed plate status, owner information, and vehicle-entry logging.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Persian License Plate Recognition System — OpenViST project profile in Image & Video AI.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when a Persian plate-reading task needs image or video input, a desktop GUI, and optionally a database-backed entry-status workflow.

Who it is for

  • Developers and operators exploring Persian license-plate recognition from images or video.
  • Teams assessing a GUI-based vehicle-entry workflow with plate status and recent-entry records.

Documented uses

  • Locate Persian plates in an image or video feed and display the detected plate crop.
  • Extract and show Persian plate characters.
  • Compare recognized plates with a database and display plate status and owner information.
  • Track resident vehicle permissions and log vehicle movements at an entrance.
Before you choose

Requirements & limits

The public GitHub repository identifies the project as open source under GPL-3.0.

  • The documented model stack is YOLOv5 for plate detection plus custom-trained models for Persian character recognition.
  • The README describes camera, video-file, and stream inputs, with source configuration through the capture parameter and config.ini.
  • The application is presented as a GUI workflow; the documentation does not describe an API.
  • The README recommends a dedicated GPU and specifies memory, processor, storage, and operating-system requirements for its stated real-time workload.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up the application and choose a video source

Install the documented Python requirements and configure a camera, video file, or stream as the processing input.

Before you start

  • Python and pip
  • A local clone of the project repository
  • For real-time processing, the README recommends an Intel Core i5 8th generation or equivalent, 8 GB RAM, a dedicated NVIDIA GTX 1060-class GPU with at least 4 GB VRAM, and 20 GB free SSD storage.

Steps

  1. Clone the repository and enter its directory.

  2. Install the packages listed in requirements.txt with pip.

  3. For a video file, set the video parameter in config.ini to its path and use the configured params.video input; for a stream, set the config.ini source to the stream address. The README also describes cv2.VideoCapture(0) as the default webcam input.

  4. Launch the application with python home-yolo.py.

Expected outcome

The application opens its GUI with the selected input source configured for plate processing.

Guide limitations

  • The README documents the setup and launch procedure but does not provide a complete deployment or database configuration recipe.
  • The listed hardware and operating-system details are project recommendations and stated compatibility, not a compatibility guarantee.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
15 October 2026
Original website title
truthofmatthew/persian-license-plate-recognition · Official homepage title · retained in its source language
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