Deep Anpr

Abstract

1. Introduction

Using neural networks to build an automatic number plate recognition system. See this blog post for an explanation.

already exist.) This step requires UKNumberPlate.ttf to be in the fonts/ directory, which can be downloaded here.

take around 100,000 batches to converge. When you're satisfied that the network has learned enough press Ctrl+C and the process will write the weights to weights.npz and return.

2. Objective

Using neural networks to build an automatic number plate recognition system

This project demonstrates how Python 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.

• 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/matthewearl/deep-anpr.git
cd deep-anpr

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 matthewearl 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.