https://github.com/aliktk/anpr-training-recognition-app
Automatic Number Plate Recognition (ANPR) Using YOLOv8 and easyOCR
https://github.com/aliktk/anpr-training-recognition-app
Last synced: 3 months ago
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Automatic Number Plate Recognition (ANPR) Using YOLOv8 and easyOCR
- Host: GitHub
- URL: https://github.com/aliktk/anpr-training-recognition-app
- Owner: Aliktk
- License: apache-2.0
- Created: 2024-09-27T13:21:20.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-10-21T17:59:07.000Z (7 months ago)
- Last Synced: 2025-01-13T14:52:51.977Z (5 months ago)
- Language: Jupyter Notebook
- Size: 25.9 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 🚗 License Plate Detection System
This project implements a License Plate Detection System using **YOLOv8** and **EasyOCR**. The system detects and localizes license plates from vehicle images and performs OCR (Optical Character Recognition) to extract the text from the detected plates. The application can be run in CLI mode for real-time detection and can also be deployed as a web-based application using **Streamlit**.
---
## 📚 Table of Contents
- [🚗 License Plate Detection System](#-license-plate-detection-system)
- [📚 Table of Contents](#-table-of-contents)
- [✨ Features](#-features)
- [📁 Project Structure](#-project-structure)
- [Prerequisites ⚙️](#prerequisites-️)
- [How to Run 🚀](#how-to-run-)
- [Running the Detection System (CLI) 🖥️](#running-the-detection-system-cli-️)
- [Running the Streamlit App 🌐](#running-the-streamlit-app-)
- [Using the Streamlit App Features 🛠️](#using-the-streamlit-app-features-️)
- [Configuration ⚙️](#configuration-️)
- [Example Configuration:](#example-configuration)
- [Example Usage 📋](#example-usage-)
- [CLI Detection for Image 📷](#cli-detection-for-image-)
- [Example Usage](#example-usage)
- [CLI Detection for Video 🎥](#cli-detection-for-video-)
- [Future Enhancements 🚀](#future-enhancements-)
- [Acknowledgements 🙏](#acknowledgements-)
- [Follow Us for Updates! 🌟](#follow-us-for-updates-)
---
## ✨ Features
- **YOLOv8-Based License Plate Detection**: Utilizes the YOLOv8 object detection model for accurate and fast license plate localization. 🎯
- **EasyOCR for Text Extraction**: Integrates EasyOCR to extract text from localized license plates. 📝
- **CLI Support**: Provides command-line functionality for detection on video files or images. 💻
- **Streamlit Integration**: Deploy a user-friendly web interface to upload and detect license plates interactively. 🌐
- **Custom Dataset**: The model is trained on a custom dataset using Roboflow for precise performance. 📊
---
## 📁 Project Structure
```
├── app/ # Streamlit app code
├── App Test Data/ # Pre-trained models or checkpoints
dataset/
├── train/
│ ├── images/
│ │ ├── image1.jpg
│ │ ├── image2.jpg
│ └── labels/
│ ├── image1.txt
│ ├── image2.txt
├── valid/
│ ├── images/
│ │ ├── image1.jpg
│ │ ├── image2.jpg
│ └── labels/
│ ├── image1.txt
├── runs/ # Model checkpoints and result logs
|── ultralytics/ # Log files for detection process
├── models/ # Pre-trained models or checkpoints
├── detect_modified.py # Main detection script with YOLOv8 and OCR
├── requirements.txt # Python dependencies
└── config.yaml
└── custom_training.ipynb
└── data.yaml
└── LICENSE
└── predict_modified.py
└── README.md
└── requirements.txt
└── train_test.ipynb
└── ultralytics
```
## Prerequisites ⚙️
Ensure you have the following installed:
- Python 3.8+ 🐍
- CUDA-enabled GPU (if you want to run it on GPU) 🖥️
- `easyocr`, `torch`, `ultralytics`, `opencv-python`, and `streamlit` Python libraries (refer to `requirements.txt`)
To install dependencies, run:
```bash
pip install -r requirements.txt
```
## How to Run 🚀
### Running the Detection System (CLI) 🖥️
You can run the License Plate Detection System in CLI mode by executing the following command:
```bash
python detect_modified.py model='ultralytics/runs/detect/train_model/weights/best.pt' source='path_to_video_or_image'
```
Replace `path_to_video_or_image` with the path to your input file (video or image). The detected license plates will be displayed, and OCR will be performed to extract the text.
## Running the Streamlit App 🌐
To interact with the detection system via a web interface, run the Streamlit app:
1. Navigate to the app directory where the `Main.py` file is located. 📁
2. Run the following command:
```bash
streamlit run app/Main.py
```
This will launch the Streamlit web app on localhost. You can upload images or videos, and the app will detect and display the results, including the extracted license plate text. 📸
---
## Future Enhancements 🚀
- **OCR Language Support:** Add support for multiple languages for OCR using EasyOCR. 🌍
- **Improved Post-processing:** Implement advanced post-processing to enhance OCR results on low-quality license plates. 🔧
- **Real-time Video Stream Support:** Extend functionality to support real-time detection from a video feed (e.g., a camera). 📹
## Acknowledgements 🙏
- **YOLOv8:** For the base model used for object detection.
- **EasyOCR:** For Optical Character Recognition.
## Follow Us for Updates! 🌟
Stay tuned for updates and enhancements to the License Plate Detection System!
- **GitHub:** [Alitktk](https://github.com/Alitktk) – Check out the repository for the latest code and releases. ⭐️
- **Feedback:** Your feedback is important! Please raise issues or suggestions on GitHub.
- **Social Media:** Follow us on [Twitter](https://www.twitter.com/engr_ali_nawaz)) and [LinkedIn](https://www.linkedin.com/in/ali-nawaz-khattak/) for the latest news and updates!
Thank you for your support! 💖