Number Plate Recognition Using Yolov11

Abstract

1. Introduction

The License Plate Recognition dataset used to train this model can be found here.

A license plate detector model is used to detect the license plates. The model was trained using YOLOv11 for 100 epochs with 21173 images of shape 640x640.

2. Objective

License plate detection using YOLOv11

This project demonstrates how Python, Computer Vision can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

• Python 3.x
• opencv_contrib_python
• opencv_python
• ultralytics

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/bhaskrr/number-plate-recognition-using-yolov11.git
cd number-plate-recognition-using-yolov11

1. Make a virtual environment using the following command:
2. Activate the virtual environment:
3. Navigate to the root directory of the project:
4. Install dependencies:
5. To execute the script, run:
6. When you're done working in the virtual environment, you can deactivate it by running:

python3 -m venv myenv

source myenv/bin/activate

cd path/to/the/project

pip install -r requirements.txt

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Deploy the model as a web app with Streamlit, Flask or FastAPI
• Compare against an additional model and report the metric difference
• Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What dataset does the project use and how was it pre-processed?
2. Which algorithm / model architecture is used and why was it chosen over alternatives?
3. How are training and testing data split, and how is overfitting avoided?
4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by bhaskrr and published on GitHub under the Apache License 2.0. Please follow the license terms and credit the original author when you use or modify this code.