YOLO-NAS-OCR-WebApp
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
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
- ‘Precision@0.50’: 0.02825159952044487
- ‘Recall@0.50’: 1.0
- ‘mAP@0.50’: 0.9623407125473022
- ‘F1@0.50’: 0.05495075136423111
Running the Web Application
- Clone the repository:
git clone https://github.com/JacobJ215/YOLO-NAS-OCR-WebApp/tree/main" cd YOLO-NAS-OCR-WebApp - Install the required dependencies:
pip install -r requirements.txt - 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.