Streamlit Based Automatic Number Plate Recognition

Abstract

1. Introduction

A streamlit based implementation of Automatic Number Plate Recognition for cars and other vehicles using images or live camera feed.

2. Objective

A streamlit based implementation of Automatic Number Plate Recognition for cars and other vehicles using images or live camera feed

This project demonstrates how Python, PyTorch 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/prateekralhan/Streamlit-based-Automatic-Number-Plate-Recognition.git
cd Streamlit-based-Automatic-Number-Plate-Recognition

1. Simply run the command pip install -r requirements.txt to install the necessary dependencies.
2. Clone this repository and install the dependencies as mentioned above.
3. Make a directory within this cloned repository with the name .streamlit (Don't forget the dot !!).
4. Create a file config.toml in this directory (Be aware of the file extension !!).
5. Copy-Paste the following contents in this file and save :
6. Navigate to the root directory of this repository and simply run the command:

[theme]
primaryColor="#ffb5b5"
backgroundColor="#132743"
secondaryBackgroundColor="#407088"
textColor="#ffb5b5"

streamlit run app.py

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 prateekralhan 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.