Transform Receipt Images into Structured Data with OCR & AI

# 🚀 Receipts Unlocked **Transform Receipt Images into Structured Data with OCR & AI** [](https://www.python.org/downloads/) [](https://opencv.org/) [](https://opensource.org/licenses/MIT)

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🚀 Features

• 📸 Extract text from receipt images using OCR
• 🤖 AI-powered data structuring
• 📊 Convert receipt data to JSON format
• 🎯 High accuracy text recognition
• ⚡ Fast processing

📋 Requirements

• Python 3.x
• OpenCV
• Pytesseract
• OpenAI API key

🛠️ Installation

1. Install Tesseract OCR

Choose your operating system

- **Windows**: Download from [UB-Mannheim/tesseract](https://github.com/UB-Mannheim/tesseract/wiki) - **Linux**: `sudo apt-get install tesseract-ocr` - **Mac**: `brew install tesseract`

2. Set Up Project

1. Clone the repository:

   git clone <repository-url>
   cd <repository-directory>
   

2. Install Python dependencies:

   pip install -r requirements.txt
   

3. Create a .env file in the project root and add your OpenAI API key:

   OPENAI_API_KEY=your_api_key_here
   

4. Update the Tesseract path in config.py to match your system’s installation.

💻 Usage

Method 1: Using the Python Module

from src.main import process_receipt

result = process_receipt("path/to/receipt.jpg")
print(result)

Method 2: Running the Script Directly

1. Add an image of a receipt named receipt.jpg to the project directory
2. Run the script:

   python main.py
   

3. The extracted JSON data will be saved in receipt.json

📊 Output Format

The program outputs a JSON object with structured receipt data. Example format:

{
  "store_name": "Example Store",
  "date": "2023-01-01",
  "items": [
    {
      "name": "Product 1",
      "price": 10.99,
      "quantity": 1
    }
  ],
  "total": 10.99
}

📁 Project Structure

• main.py: The main script that processes the image, extracts text, and converts it to JSON
• src/: Source code directory
• requirements.txt: List of required Python packages
• config.py: Configuration settings including Tesseract path
• README.md: Project documentation

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.