Ha Openalpr

Abstract

1. Introduction

This docker container is designed to read GB Number plates from a mpeg4 video stream. Please ensure your camera is either designed for number plate recognition or you have a suitably configured high specification camera. I can't offer any advice on the camera configuration as there are too many settings and too many cameras on the market.

It is specifically designed to call Home Assistant using the API and will execute a script called script.ANPR. Create this script in HA and put what ever automation you want in it.

When it recognises a number plate it will then enter a time out ensuring that the same number plate does not fire the script multiple times. It will just as easy call another api of your choice with a few little changes.

2. Objective

OpenALPR Number Plate Recognition for Home Assistant

This project demonstrates how C++ can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

5. System Requirements

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

• Arduino IDE / PlatformIO or a C++ compiler (g++)
• Target board (e.g. Arduino, ESP32) where applicable
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/samtwilliams/ha-openalpr.git
cd ha-openalpr

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 samtwilliams and published on GitHub under the GNU Affero General Public License v3.0. Please follow the license terms and credit the original author when you use or modify this code.