Invoice Intelligence with Learning Loop

An OCR-based invoice processing system built with FastAPI for extracting and managing invoice data.

Folder Structure

Teera_ai/
├── app/
│   ├── app.py                      # FastAPI main application entry point
│   ├── invoice_feedback.db         # SQLite database for feedback storage
│   ├── controllers/
│   │   ├── data_extraction.py      # Data extraction and parsing logic
│   │   ├── db_config.py            # Database configuration and setup
│   │   ├── feedback.py             # Feedback management functionality
│   │   ├── invoice_controller.py   # API endpoints for invoice operations
│   │   └── ocr.py                  # OCR processing and recognition
│   ├── models/
│   │   └── models.py               # Data models and schemas
│   ├── static/
│   │   ├── custom_css.css          # Custom styling
│   │   ├── custom_js.js            # Frontend JavaScript logic
│   │   └── invoices/               # Invoice storage directory
│   └── templates/
│       └── index.html              # Main HTML interface
├── data/
│   ├── OCR.docx                    # Documentation and notes
│   ├── pdf_sample_invoice.pdf      # Sample invoice for testing
├── notebook/
│   ├── Donut-ITT.ipynb            # Donut model research and experiments
│   ├── OCR-R&D.ipynb              # OCR research and development
│   ├── Sample-LayoutLmv3_training_invoice_dataset.ipynb  # LayoutLMv3 training notebook
│   └── invoice_output/            # Output storage for notebook experiments
├── README.md                        # This file
└── requirements.txt                 # Python dependencies

About This Project

This is a system designed to automate invoice processing using modern OCR and deep learning techniques. The project combines multiple approaches to extract structured data from invoice documents. It includes a frontend interface for uploading and managing invoices, backend API services for processing, and research notebooks exploring different ML models for document analysis. The system stores extracted data in a database and allows users to provide feedback on accuracy for model improvements.

Setup Instructions

Prerequisites

• Python 3.8 or higher
• pip (Python package manager)
• Virtual environment tool (venv or conda)
• Git

Step 1: Clone the Repository

git clone https://github.com/bilalhameed248/Invoice-Intelligence-With-Learning-Loop.git
cd Teera_ai

Step 2: Create Virtual Environment

Using venv:

python -m venv venv

Activate virtual environment:

• On Windows:

venv\Scripts\activate

• On macOS/Linux:

source venv/bin/activate

Step 3: Install Dependencies

pip install -r requirements.txt

Step 4: Configure Database

The SQLite database is automatically initialized on first run. If you need to manually set it up:

cd app
python -c "from controllers.db_config import create_db; create_db()"
cd ..

Getting Started

Run the application using:

cd app
uvicorn app:app --host 0.0.0.0 --port 3636 --reload

Access the application at http://localhost:3636

Using the Application

1. Open your browser and navigate to http://localhost:3636
2. Upload an invoice PDF or image file
3. The system will process the invoice and extract:
   • Vendor information
   • Invoice number and date
   • Line items and amounts
   • Tax calculations
   • Suggested accounting entries
4. Review the extracted data and provide feedback for model improvement

Troubleshooting

Port Already in Use:

uvicorn app:app --host 0.0.0.0 --port 8000 --reload

Module Not Found Errors: Make sure the virtual environment is activated and all dependencies are installed:

pip install -r requirements.txt

Database Issues: Reset the database by removing app/invoice_feedback.db and restarting the application