Indian Number Plate Recognition

License plate recognition of Indian vehicles using YOLOv4 and keras-ocr

AI & Machine LearningPythonApache-2.0

Abstract

Indian Number Plate Recognition is an open-source AI & Machine Learning project. License plate recognition of Indian vehicles using YOLOv4 and keras-ocr.    absl-py==0.9.0    easydict==1.9    keras_ocr==0.8.3    opencv-python==4.2.0.34    tensorflow==2.2.0. It is built using Python. The complete source code is publicly available on GitHub under the Apache License 2.0, making it a useful reference for students building an AI & Machine Learning mini project or final-year project.

1. Introduction

   absl-py==0.9.0    easydict==1.9    keras_ocr==0.8.3    opencv-python==4.2.0.34    tensorflow==2.2.0

  Download pretrained model from here and copy it inside "data" folder. #Run demo on sample video with default arguments python demo_video.py

#Run demo with command line arguments python demo_video.py --input "Input_video_path" --output "Path_to_save_result" --size "frame_size"

2. Objective

License plate recognition of Indian vehicles using YOLOv4 and keras-ocr

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

4. Technology Stack

Python

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/keshavoct98/Indian-Number-Plate-Recognition.git
cd Indian-Number-Plate-Recognition

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 keshavoct98 and published on GitHub under the Apache License 2.0. Please follow the license terms and credit the original author when you use or modify this code.

View Source on GitHubDownload ZIP

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