This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle
A small command-line tool that reads receipt and invoice images and returns clean, structured data: vendor, date, total, currency, and expense category.
Who would pay for it: freelancers and small-business owners who spend every quarter digging through photos and screenshots of receipts before tax season. A bookkeeper handling several clients' receipts could use it too.
The problem it solves: manual data entry is slow and tedious, and receipts contain client names and payment details that people don't want to upload to a cloud service. This tool turns a folder of messy images into structured data, and everything stays on the user's machine.
Example output:
{"file": "receipt_01.jpg", "vendor": "Starbucks", "date": "2024-03-12", "total": 8.45, "currency": "USD", "category": "meals"}
A tiny script that reads receipt images with an open-weight vision model running locally via Ollama and returns structured data: vendor, date, total, currency, and expense category.
No cloud API, no per-page fees, and receipts never leave your machine.
ollama pull qwen2.5vl:7b
pip install ollama pydantic
python receipt_extract.py receipt.jpg
python receipt_extract.py ./receipts --csv results.csv
python receipt_extract.py ./receipts --model gemma3:4b
Example output (one JSON line per receipt):
{"file": "receipt.jpg", "vendor": "Starbucks", "date": "2024-03-12", "total": 8.45, "currency": "USD", "category": "meals"}
Usage is one line:
python receipt_extract.py ./receipts --csv results.csv
The model is a command-line flag (--model gemma3:4b), so swapping models needs no code changes.
Where it fell short: small local models can misread blurry or unusual receipts. A larger closed model would probably do better on those. For a tool that handles sensitive financial documents, I think the privacy and zero-cost trade is worth it, as long as the output is spot-checked before it goes to an accountant.