Automatic Number Plate Recognition using YOLO-NAS and EasyOCR (Images & Videos)

This project uses YOLO-NAS and EasyOCR to detect license plates and perform Optical Character Recognition (OCR) on them. The project includes both image and video processing capabilities, and has been deployed as a Streamlit web application. This is an update to a previous project, Optical-Character-Recognition-WebApp

GitHub

Features

• Real-time license plate detection using YOLO-NAS
• Optical Character Recognition (OCR) using EasyOCR
• Interactive user interface built with Streamlit

Dataset

The dataset used for training and testing the YOLO-NAS model contains 484 annotated images of cars with license plates. The images were sourced from "Brave Images", "Google Images", and "https://flickr.com/". The annotations were made using RoboFlow.

Project Overview

This project builds upon an earlier version that used YOLO-v5 and InceptionResNetV2 . The major changes and updates in this version include:

• Transition from YOLO-v5 to YOLO-NAS for license plate detection
• Replacement of pytesseract with EasyOCR for more accurate text extraction
• Training the YOLO-NAS model for 15 epochs using Google Colab
• Deployment as a Streamlit web application

Evaluation

• 'PPYoloELoss/loss_cls': 0.9181855

• 'PPYoloELoss/loss_iou': 0.17447565

• 'PPYoloELoss/loss_dfl': 0.9500977

• 'PPYoloELoss/loss': 1.8294234

• '[email protected]': 0.02825159952044487

• '[email protected]': 1.0

• '[email protected]': 0.9623407125473022

• '[email protected]': 0.05495075136423111

Running the Web Application

1. Clone the repository:

git clone https://github.com/JacobJ215/YOLO-NAS-OCR-WebApp/tree/main"
cd YOLO-NAS-OCR-WebApp

2. Install the required dependencies:

pip install -r requirements.txt

3. Run the Streamlit app:

streamlit run app.py

Usage

The web application provides two main modes: Run on Image: Upload an image containing a license plate, and the application will perform real-time detection and OCR, displaying the extracted text from the license plate.

Run on Image

Run on Video: Upload a video with license plates, and the application will perform real-time detection and OCR on the video frames, showing the extracted text and the frame rate.

Run on Video

Acknowledgments

Inspired by https://github.com/MuhammadMoinFaisal

The YOLO-NAS model used in this project is based on Super-Gradients Repository.

EasyOCR is an excellent OCR library developed by Jaided AI.