Automatic Number Plate Recognition

Automatic Number Plate Recognition with YOLOv5 and PyTorch

AI & Machine LearningJupyter NotebookMIT

Abstract

Automatic Number Plate Recognition is an open-source AI & Machine Learning project. Automatic Number Plate Recognition with YOLOv5 and PyTorch. 1) opencv-python 4.5.3.56 or above 2) numpy 1.20.0 or above 3) python 3.9 or above 4) numpy 1.20.0 or above 5) pytorch 1.10.0 or above. It is built using Jupyter Notebook, Deep Learning, OpenCV, PyTorch. The complete source code is publicly available on GitHub under the MIT License, making it a useful reference for students building an AI & Machine Learning mini project or final-year project.

1. Introduction

1) opencv-python 4.5.3.56 or above 2) numpy 1.20.0 or above 3) python 3.9 or above 4) numpy 1.20.0 or above 5) pytorch 1.10.0 or above

The goal of this project was to create a model that can accurately detect number plates on cars and bikes. This model can be used along with the video data generated from CCTV cameras that are installed on highways and roadways to detect number plate of vechicles that commit traffic violations.

2. Objective

Automatic Number Plate Recognition with YOLOv5 and PyTorch

This project demonstrates how Jupyter Notebook, Deep Learning, OpenCV can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

Jupyter NotebookDeep LearningOpenCVPyTorch

5. System Requirements

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

  • Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
  • pip for dependencies
  • Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/wasdac9/automatic-number-plate-recognition.git
cd automatic-number-plate-recognition

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 wasdac9 and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.

View Source on GitHubDownload ZIP

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