Deep License Plate Recognition

Automatic License Plate Recognition (ALPR) or Automatic Number Plate Recognition (ANPR) software that works with any camera.

AI & Machine LearningC++MIT

Abstract

Deep License Plate Recognition is an open-source AI & Machine Learning project. Automatic License Plate Recognition (ALPR) or Automatic Number Plate Recognition (ANPR) software that works with any camera. Use the quickstart below to recognize a plate in an image, or jump to the repository map to find a tool for another workflow. It is built using C++, Machine Learning, Deep Learning. 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

Use the quickstart below to recognize a plate in an image, or jump to the repository map to find a tool for another workflow.

The command returns JSON containing the recognized plate, confidence scores, and bounding box. The complete response schema is in the Snapshot API reference.

2. Objective

Automatic License Plate Recognition (ALPR) or Automatic Number Plate Recognition (ANPR) software that works with any camera.

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

4. Technology Stack

C++Machine LearningDeep Learning

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/parkpow/deep-license-plate-recognition.git
cd deep-license-plate-recognition
uv sync --locked
uv run pre-commit install
uv run pre-commit run --all-files

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 parkpow 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

Want to build this as your internship project?

Work on an AI & Machine Learning project like this with mentor guidance, weekly reviews and an internship certificate from Training Trains, Erode — online or offline.

Apply for AI & Machine Learning Internship