Invoice Net

Deep neural network to extract intelligent information from invoice documents.

AI & Machine LearningPythonMIT

Abstract

Invoice Net is an open-source AI & Machine Learning project. Deep neural network to extract intelligent information from invoice documents. The InvoiceNet logo was designed by Sidhant Tibrewal. Check out his work for some more beautiful designs. It is built using Python, Deep Learning, Keras. 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

The InvoiceNet logo was designed by Sidhant Tibrewal. Check out his work for some more beautiful designs.

InvoiceNet has been developed and tested on Ubuntu 20.04 with CUDA Version: 11.8, cuDNN version: 8.9.7, and Tensorflow v2.13.1.

The install.sh script will install all the dependencies, create a virtual environment, and install InvoiceNet in the virtual environment.

2. Objective

Deep neural network to extract intelligent information from invoice documents.

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

4. Technology Stack

PythonDeep LearningKeras

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/naiveHobo/InvoiceNet.git
cd InvoiceNet

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 naiveHobo 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

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