Alpr

:camera: Golang web server for vehicle number plates recognition

AI & Machine LearningGoMIT

Abstract

Alpr is an open-source AI & Machine Learning project. :camera: Golang web server for vehicle number plates recognition. It is built using Go. 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

Golang web server for vehicle number plates recognition

Mount current directory and use bash inside docker container for development

2. Objective

:camera: Golang web server for vehicle number plates recognition

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

4. Technology Stack

Go

5. System Requirements

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

  • Go 1.20 or later
  • Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/opencars/alpr.git
cd alpr
http http://localhost:8080/api/v1/alpr/private/recognize?image_url="https://img03.platesmania.com/170327/o/9629055.jpg"
[
    {
        "coordinates": [
            {
                "x": 185,
                "y": 421
            },
            {
                "x": 302,
                "y": 430
            },
            {
                "x": 299,
                "y": 459
            },
            {
                "x": 183,
                "y": 448
            }
        ],
        "plate": "AA9359PC"
    }
]

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 opencars 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.

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