Receipt OCR Engine

An efficient OCR engine for receipt image processing.

This repository provides a comprehensive solution for Optical Character Recognition (OCR) on receipt images, featuring both a dedicated Tesseract OCR module and a general receipt processing package using LLMs.

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Table of Contents

• Receipt OCR Engine
  • Star History
  • Table of Contents
  • Quick Start
  • Project Structure
  • Prerequisites
  • How to Use Receipt OCR
    • Receipt OCR Module (Structured Data Extraction)
    • Tesseract OCR Module (Raw Text Extraction)
  • Troubleshooting
  • Contributing
  • LinkedIn Post
  • License

Quick Start

Extract structured data from a receipt in 3 steps:

1. Install the package:

   pip install receipt-ocr

2. Set up your API key:

   export OPENAI_API_KEY="your_openai_api_key_here"

3. Process a receipt:

   receipt-ocr images/receipt.jpg

For Docker or advanced usage, see How to Use Receipt OCR below.

Project Structure

The project is organized into two main modules:

• src/receipt_ocr/: A new package for abstracting general receipt processing logic, including CLI, programmatic API, and a production FastAPI web service for LLM-powered structured data extraction from receipts.
• src/tesseract_ocr/: Contains the Tesseract OCR FastAPI application, CLI, utility functions, and Docker setup for performing raw OCR text extraction from images.

Prerequisites

• Python 3.x
• Docker & Docker-compose(for running as a service)
• Tesseract OCR (for local Tesseract CLI usage) - Installation Guide

How to Use Receipt OCR

Receipt OCR Module (Structured Data Extraction)

This module provides a higher-level abstraction for processing receipts, leveraging LLMs for parsing and extraction.

To use the receipt-ocr CLI, first install it:

pip install receipt-ocr

1. Configure Environment Variables: Create a .env file in the project root or set environment variables directly. This module supports multiple LLM providers.

   Supported Providers:

   • OpenAI:

     Get API key from: https://platform.openai.com/api-keys

     OPENAI_API_KEY="your_openai_api_key_here"
     OPENAI_MODEL="gpt-4o"
     

   • Gemini (Google):

     Get API key from: https://aistudio.google.com/app/apikey

     OPENAI_API_KEY="your_gemini_api_key_here"
     OPENAI_BASE_URL="https://generativelanguage.googleapis.com/v1beta/openai/"
     OPENAI_MODEL="gemini-2.5-pro"
     

   • Groq:

     Get API key from: https://console.groq.com/keys

     OPENAI_API_KEY="your_groq_api_key_here"
     OPENAI_BASE_URL="https://api.groq.com/openai/v1"
     OPENAI_MODEL="llama3-8b-8192"
     

2. Process a receipt using the receipt-ocr CLI:

   receipt-ocr images/receipt.jpg

   This command will use the configured LLM provider to extract structured data from the receipt image.

   sample output

   {
     "merchant_name": "Saathimart.com",
     "merchant_address": "Narephat, Kathmandu",
     "transaction_date": "2024-05-07",
     "transaction_time": "09:09:00",
     "total_amount": 185.0,
     "line_items": [
       {
         "item_name": "COLGATE DENTAL",
         "item_quantity": 1,
         "item_price": 95.0,
         "item_total": 95.0
       },
       {
         "item_name": "PATANJALI ANTI",
         "item_quantity": 1,
         "item_price": 70.0,
         "item_total": 70.0
       },
       {
         "item_name": "GODREJ NO 1 SOAP",
         "item_quantity": 1,
         "item_price": 20.0,
         "item_total": 20.0
       }
     ]
   }

3. Using Receipt OCR Programmatically in Python:

   You can also use the receipt-ocr library directly in your Python code:

   from receipt_ocr.processors import ReceiptProcessor
   from receipt_ocr.providers import OpenAIProvider
   
   # Initialize the provider
   provider = OpenAIProvider(api_key="your_api_key", base_url="your_base_url")
   
   # Initialize the processor
   processor = ReceiptProcessor(provider)
   
   # Define the JSON schema for extraction
   json_schema = {
       "merchant_name": "string",
       "merchant_address": "string",
       "transaction_date": "string",
       "transaction_time": "string",
       "total_amount": "number",
       "line_items": [
           {
               "item_name": "string",
               "item_quantity": "number",
               "item_price": "number",
           }
       ],
   }
   
   # Process the receipt
   result = processor.process_receipt("path/to/receipt.jpg", json_schema, "gpt-4.1")
   
   print(result)

   Advanced Usage with Response Format Types:

   For compatibility with different LLM providers, you can specify the response format type:

   result = processor.process_receipt(
       "path/to/receipt.jpg", 
       json_schema, 
       "gpt-4.1", 
       response_format_type="json_object"  # or "json_schema", "text"
   )

   Supported response_format_type values:

   • "json_object" (default) - Standard JSON object format
   • "json_schema" - Structured JSON schema format (for newer OpenAI APIs)
   • "text" - Plain text responses
   Using json_schema format

   When using response_format_type="json_schema", you must provide a proper JSON Schema object (not the simple dictionary format). The library handles the OpenAI API boilerplate, so you just need to pass the schema definition.

   Example proper JSON Schema:

   json_schema = {
     "type": "object",
     "properties": {
       "merchant_name": {"type": "string"},
       "merchant_address": {"type": "string"},
       "transaction_date": {"type": "string"},
       "transaction_time": {"type": "string"},
       "total_amount": {"type": "number"},
       "line_items": {
         "type": "array",
         "items": {
           "type": "object",
           "properties": {
             "item_name": {"type": "string"},
             "item_quantity": {"type": "number"},
             "item_price": {"type": "number"}
           },
           "required": ["item_name", "item_quantity", "item_price"],
           "additionalProperties": false
         }
       }
     },
     "required": [
       "merchant_name",
       "merchant_address",
       "transaction_date",
       "transaction_time",
       "total_amount",
       "line_items"
     ],
     "additionalProperties": false
   }

   See the OpenAI structured outputs documentation for more information.

4. Run Receipt OCR as a Docker web service:

   For a production-ready REST API, use the FastAPI web service:

   docker compose -f app/docker-compose.yml up

   The service provides REST endpoints for receipt processing:

   • GET /health - Health check
   • POST /ocr/ - Process receipt images with optional custom JSON schemas

   Example API usage:

   # Health check
   curl http://localhost:8000/health
   
   # Process receipt with default schema
   curl -X POST "http://localhost:8000/ocr/" \
     -F "file=@images/receipt.jpg"
   
   # Process with custom schema
   curl -X POST "http://localhost:8000/ocr/" \
     -F "file=@images/receipt.jpg" \
     -F 'json_schema={"merchant": "string", "total": "number"}'

   For detailed API documentation, visit http://localhost:8000/docs when the service is running.

Tesseract OCR Module (Raw Text Extraction)

This module provides direct OCR capabilities using Tesseract. For more detailed local setup and usage, refer to src/tesseract_ocr/README.md.

1. Run Tesseract OCR locally via CLI:

   python src/tesseract_ocr/main.py -i images/receipt.jpg

   Replace images/receipt.jpg with the path to your receipt image.

   Please ensure that the image is well-lit and that the edges of the receipt are clearly visible and detectable within the image.

2. Run Tesseract OCR as a Docker service:

   docker compose -f src/tesseract_ocr/docker-compose.yml up

   Once the service is up and running, you can perform OCR on receipt images by sending a POST request to http://localhost:8000/ocr/ with the image file.

   API Endpoint:

   • POST /ocr/: Upload a receipt image file to perform OCR. The response will contain the extracted text from the receipt.

   Note: The Tesseract OCR API returns raw extracted text from the receipt image. For structured JSON output with parsed fields such as merchant name, line items, and totals, use the receipt-ocr instead.

   Example usage with cURL:

   curl -X 'POST' \
     'http://localhost:8000/ocr/' \
     -H 'accept: application/json' \
     -H 'Content-Type: multipart/form-data' \
     -F 'file=@images/paper-cash-sell-receipt-vector-23876532.jpg;type=image/jpeg'

Troubleshooting

Common Issues and Solutions:

• API Key Errors: Ensure your OPENAI_API_KEY is set correctly and has sufficient credits. Check the provider's dashboard for key status.

• Model Not Found: Verify the OPENAI_MODEL matches available models for your provider. For OpenAI, check https://platform.openai.com/docs/models.

• Poor OCR Results: Use high-quality, well-lit images. Ensure receipt text is clear and not skewed.

• Installation Issues: If pip install receipt-ocr fails, try pip install --upgrade pip first.

• Docker Issues: Ensure Docker is running and ports 8000 are available.

For more help, start a GitHub Discussion to ask questions, or create a new issue if you found a bug.

Contributing

We welcome contributions to the Receipt OCR Engine! To contribute, please follow these steps:

1. Fork the repository and clone it to your local machine.

2. Create a new branch for your feature or bug fix.

3. Set up your development environment:

   # Navigate to the project root
   cd receipt-ocr
   
   # Install uv
   curl -LsSf https://astral.sh/uv/install.sh | sh # OR pip install uv
   
   # Create and activate a virtual environment
   uv venv --python=3.12
   source .venv/bin/activate  # For Windows, use .venv\Scripts\activate
   
   # Install development and test dependencies
   uv sync --all-extras --dev
   uv pip install -e.
   
   # Optional: Install requirements for the tesseract_ocr module
   uv pip install -r src/tesseract_ocr/requirements.txt

4. Make your changes and ensure they adhere to the project's coding style.

5. Run tests to ensure your changes haven't introduced any regressions:

   # Run tests for the receipt_ocr module
   uv run pytest tests/receipt_ocr
   
   # Run tests for the tesseract_ocr module  
   uv run pytest tests/tesseract_ocr

6. Run linting and formatting checks:

   uvx ruff check .
   uvx ruff format .

7. Commit your changes with a clear and concise commit message.

8. Push your branch to your forked repository.

9. Open a Pull Request to the main branch of the upstream repository, describing your changes in detail.

LinkedIn Post

• Gemini Docs: https://ai.google.dev/tutorials/python_quickstart
• LinkedIn Post: https://www.linkedin.com/feed/update/urn:li:activity:7145860319150505984/

License

This project is licensed under the terms of the MIT license.