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This project is used to detect the license plate of the vehicle in real time, trained using Car Detection Licence Plate dataset available on Kaggle. Used yolov4 because it performs much better than traditional cv techniques and then used EasyOCR to extract text from the number plate. Please see readme for details.

REAL TIME Automatic Number Plate Recognition with Python and AWS | Object detection and Tracking | Yolov8 Sort

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Real Time Adaptive Traffic Filtering with Number Plate Detection and Character recognition using Image Processing

Parkode is a smart parking lot monitoring system that processes live video frames from surveillance cameras to provide real-time information on occupied/vacant spots, spot overlap violations, foreign objects detection and automatic number plate recognition.

Real Time Number Plate Recognition by Jillani Soft Tech

A local ANPR system on Raspberry Pi using YOLO and OCR for real-time number plate detection and recognition, optimized for edge deployment with low latency and no cloud dependency.

Created an Automatic Number Plate Recognition (ANPR) system using TensorFlow and EasyOCR. Capture real-time images via webcam, detect plates with TensorFlow Object Detection, and extract text using EasyOCR. This project ensures accurate plate detection and text extraction for traffic management and security applications.

A multithreaded Automatic Number Plate Recognition (ANPR) server written in Python. This code is mainly meant as a proof-of-concept and structure layout test for the C++ implementation, Python is NOT fast enough for real-time processing of images with 30+ fps, and properly cross-platform multi-threading is hard due to the Global Interpreter Lock (GIL).

ANPD is an automatic number plate detection system using two approaches: one with deep learning, OpenCV, and MongoDB for efficient data storage, and another with OpenCV and EasyOCR for real-time number plate recognition. Both systems capture and process license plate images to extract and display plate numbers seamlessly

Automatic Number Plate Recognition (ANPR) & Traffic Classification (ATCC) system using YOLOv10 and Tesseract OCR. Real-time vehicle detection, license plate extraction, and data storage in SQLite. Infosys Springboard Project.

🔐 Zenpark – Smart Parking Management System Zenpark is an AI-powered smart parking solution that uses YOLO for vehicle detection and OCR for number plate recognition. It features a real-time monitoring system with FastAPI backend, MongoDB & MySQL for data management, and a React Native frontend for seamless access and control.

A full-featured Flutter OCR module for real-time number plate, VIN, document, and custom text recognition using Google ML Kit. Includes a modern UI, glassmorphic design, and supports Android & iOS.

This project focuses on the identification of five essential attributes of any vehicle: type, color, damage status, speed, and license plate number. Leveraging the power of computer vision and machine learning, we have implemented YOLOv8 to achieve accurate and real-time attribute recognition.

Building, deploying, and comparing a real-time licence plate recognition system with YOLOv5, YOLOv11, and OCRs

Automatic Number Plate Recognition (ANPR) using Tensorflow and EasyOCR is a project that uses machine learning tools to recognize and extract license plate numbers from images or videos. Tensorflow is used for object detection and EasyOCR is used for optical character recognition (OCR) to accurately identify the license plate numbers.

Number Plate Text Recognition in Real Time

Automatic Number Plate Recognition (ANPR) system using YOLOv8 for real-time global license plate detection and recognition.

🚗 Real-Time Automatic Number Plate Recognition (ANPR) System built with YOLOv8 and EasyOCR — detects license plates, reads them using OCR, logs details with confidence scores, and stores snapshots locally using GPU acceleration.

This project implements a real-time Automatic Number Plate Recognition (ANPR) system using: - YOLOv8 → for accurate license plate detection - PaddleOCR → for text extraction from detected plates

Python-based Automatic Number Plate Recognition (ANPR) system uses OpenCV and OCR to detect, read, and log vehicle plates in real time. Ideal for traffic enforcement, parking management, and security surveillance, it processes videos/images, flags suspicious plates, and securely stores data for quick access.

This project implements a License Plate Recognition system using the YOLOv8 algorithm, designed to detect and recognize number plates of four-wheelers in real-time from a live feed. The model achieves high accuracy in identifying and extracting license plate information thus highlighting robustness of the YOLOv8 algorithm in dynamic environment.

Building a real-time number plate recognition system, and will work with you to alter the purpose (like integrate with CCTV cameras, will make an mobile or web app for commercial purpose) as per requirement.

The Automatic Number Plate Detection project is an intelligent system developed using Python and machine learning techniques to detect and recognize number plates in images or video streams. This project utilizes the power of computer vision and deep learning algorithms to automatically extract license plate information from vehicle images.

1. Detect license plates from images and in real time from video 2. Apply a EasyOCR to license plates to extract the plate number 3. Save license plates detected for future analysis and searching

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Automatic Number Plate Recognition Real Time

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Real-time Automatic Number Plate Recognition (ANPR) leveraging Machine Learning, Flask web application, and configurable detection thresholds.