persian-license-plate-recognition and ANPR-System

ANPR-System
42
Emerging
Maintenance 0/25
Adoption 10/25
Maturity 16/25
Community 25/25
Maintenance 6/25
Adoption 6/25
Maturity 13/25
Community 17/25
Stars: 450
Forks: 129
Downloads:
Commits (30d): 0
Language: Python
License: GPL-3.0
Stars: 20
Forks: 9
Downloads:
Commits (30d): 0
Language: Jupyter Notebook
License: MIT
Stale 6m No Package No Dependents
No Package No Dependents

About persian-license-plate-recognition

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.

This system helps security personnel, property managers, or traffic monitors automatically identify Persian license plates from live video feeds or recorded footage. It takes a video stream or image as input and outputs the detected license plate image, its recognized Persian characters, the vehicle owner's name, and the plate's access status (e.g., allowed or not allowed). This is designed for anyone needing to control vehicle access or track vehicle movements efficiently in regions using Persian license plates.

traffic-monitoring access-control vehicle-identification security-management property-management

About ANPR-System

Runoi/ANPR-System

High-accuracy Russian ANPR system built with YOLOv8 for detection and an optimized, custom-trained PyTorch CRNN for OCR.

This system helps identify and read Russian vehicle license plates from images or live video feeds. It takes any image or video containing Russian license plates as input and outputs the recognized plate numbers. It's designed for anyone needing to automate the identification of vehicles based on their license plates, such as security personnel, traffic analysts, or parking managers.

traffic-management vehicle-identification security-monitoring parking-enforcement surveillance

Scores updated daily from GitHub, PyPI, and npm data. How scores work