Anpr Dashcam

A simple automatic number plate recognition system

AI & Machine LearningJavaScriptMIT

Abstract

Anpr Dashcam is an open-source AI & Machine Learning project. A simple automatic number plate recognition system. This application takes photos of the road ahead, scans for a number plate and displays any information found from the DVLA (UK) for that number plate. It is built using JavaScript, Raspberry Pi. 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

This application takes photos of the road ahead, scans for a number plate and displays any information found from the DVLA (UK) for that number plate.

2. Objective

A simple automatic number plate recognition system

This project demonstrates how JavaScript, Raspberry Pi can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

JavaScriptRaspberry Pi

5. System Requirements

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

  • Node.js (LTS) and npm
  • A modern web browser
  • VS Code or any code editor
  • Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/kslat3r/anpr-dashcam.git
cd anpr-dashcam

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 kslat3r 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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