receipt-processor 0.1.1

Process and analyze receipt data

Receipt Processor

Receipt Processor is a Python package for extracting, structuring, and analyzing receipt data. It combines OCR, database storage, budget tracking, and a Streamlit interface so you can convert receipt images into searchable spending records.

What it does

• Extracts text from receipt images using EasyOCR and using that text for LLM parsing
• Stores receipts and item details in SQLite databases
• Provides spending analysis by total, month, category, and vendor
• Supports budget tracking for monthly and category budgets
• Includes a Streamlit app for upload, review, and manual correction

Key Features

• OCR extraction for receipt images and PDFs
• LLM-guided item categorization with a preset category vocabulary
• Database ingestion using receipt_processor.db_ingest
• Reporting and analytics using receipt_processor.db_queries
• Interactive app in app.py for visual review and budget monitoring

Installation

Install from PyPI

When published, you can install Receipt Processor directly from PyPI:

pip install receipt-processor

Install from source

For development or the latest repository version, use uv to create an environment and install dependencies:

pip install uv
cd /path/to/Receipt-Processor
uv sync

After uv sync completes, activate the created virtual environment:

# Windows
.venv\Scripts\activate

# macOS/Linux
source .venv/bin/activate

Quick Start

Run the Streamlit interface:

streamlit run app.py

Then provide database paths such as:

• data/receipts.db
• data/budget.db

The app can create these databases automatically.

Basic Usage

Extract OCR text

from receipt_processor.ocr_utils import extract_text_from_image
text = extract_text_from_image("receipts/my_receipt.png")
print(text)

Initialize databases

from receipt_processor.db_ingest import initialize_database, initialize_budget_database
initialize_database(db_path="data/receipts.db")
initialize_budget_database(db_path="data/budget.db")

Insert receipt data

from receipt_processor.db_ingest import insert_receipt
receipt_id = insert_receipt(
    vendor="Walmart",
    date="2024-01-15",
    time="14:30:00",
    total_amount=45.99,
    tax_amount=3.50,
    items=[
        {"item_name": "Milk", "price": 3.99, "category": "Grocery"},
        {"item_name": "Bread", "price": 2.49, "category": "Grocery"},
    ],
    db_path="data/receipts.db"
)

Query spending

from receipt_processor.db_queries import get_total_spending, get_category_breakdown
print(get_total_spending(db_path="data/receipts.db"))
print(get_category_breakdown(db_path="data/receipts.db"))

Notes

• Automatic categorization uses an LLM prompt with a fixed set of categories.
• Batch receipt processing is designed for simple single-page receipts without refunds.
• Complex receipts are safer to process manually using the insert workflow.

Documentation

A more detailed tutorial is available in tutorial.qmd and via the generated GitHub Pages site. The repository also includes API documentation in api.qmd.

Links

• Documentation source: api.qmd
• Tutorial source: tutorial.qmd
• Report source: technical-report.qmd
• Streamlit app: run streamlit run app.py
• GitHub repository: https://github.com/Xapamma/Receipt-Processor

Repository Layout

• app.py — Streamlit front-end for receipt upload and review
• cat_try.py — category classification and LLM prompt logic
• ocr_png_to_text.py — OCR extraction helpers
• src/receipt_processor/ — core package implementation
• scripts/ — example scripts for extraction, ingestion, and validation
• _quarto.yml — website publishing configuration
• pyproject.toml — project dependencies and metadata