# Receipt Sorter: a private, offline bookkeeping helper for freelancers, powered by Qwen2.5-VL > Source: > Published: 2026-10-17 11:55:59+00:00 *This is a submission for the [Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle](https://dev.to/challenges/hacktoberfest-week2-2026-10-12)* **receipt-sorter** is a command-line tool that turns a messy folder of receipts and invoices (PDFs and phone photos) into something an accountant can use. It renames each file (`2026-10-14_Brew-Bean-Cafe_441.0.png`), writes an accountant-ready `receipts.csv` (date, vendor, total, tax, currency, category), and produces a `summary.md` with totals per category. **Who would pay for it:** freelancers, consultants and tiny agencies who dread tax season and handle other people's financial documents. Their problem is twofold: the sorting is tedious, and they don't want client invoices and bank details uploaded to a third-party cloud. A tool that runs entirely on their own laptop solves both. Drop a folder of receipts and invoices (PDFs and phone photos) in, get back renamed files an accountant-ready `receipts.csv`, and a category summary. A local open-weight vision model does the reading, so **client invoices and bank details never leave your laptop**. ``` sorted_output/ sorted/ 2026-03-04_Figma_15.0.pdf ... (confident results) needs_review/ anything unreadable, low-confidence or possibly duplicated receipts.csv date, vendor, total, tax, currency, category, notes ... summary.md totals per currency and category + a review list ``` Originals are copied, never moved or modified. ``` # 1. Install Ollama (https://ollama.com) and pull an open vision model ollama pull qwen2.5vl:7b # ~6 GB; use gemma3:4b or qwen2.5vl:3b on smaller machines # 2. Install deps pip install -r requirements.txt # 3. Run python -m sorter ~/Documents/receipts -o ~/Documents/receipts_sorted python -m sorter ./receipts --model gemma3:4b --dry-run # preview, writes nothing ``` Add `--verify` to read every document twice (second pass at a different… `₹1,234.50` and `1.234,50` are normalised, dates are converted to ISO, tax greater than total is flagged, and unknown categories are caught. Anything uncertain goes to a `needs_review/` folder rather than being silently accepted. Exact duplicates are skipped by SHA-256 without spending any inference.`categories.json` holds your own categories and vendor rules (which always override the model), and `examples.json` holds corrected examples used as few-shot guidance. `--model` swaps in any Ollama vision model. I generated seven fake receipts to test with: a café bill in INR, a SaaS invoice in USD, a tilted Uber photo, a German hardware-store receipt with comma decimals, an internet bill, a deliberately unreadable one, and an exact duplicate. On my first run, the model got the first five right: vendor, date, total, currency and category, including the tilted photos and the German number format. The duplicate was skipped. The deliberately blurry receipt **slipped through**. The real image read `1?/09/2026` and `3?5.00`, but the model confidently returned `2026-09-17` and `375.0`, with no warning. For a bookkeeping tool, a quiet wrong number is worse than a flagged one. I added two safeguards: `?`, an unparseable quote, or a quote that disagrees with the extracted value gets flagged.`--verify` mode: **Performance:** [YOUR LAPTOP: RAM, CPU/GPU] processed the 7 documents in about [X] minutes without `--verify` and [Y] minutes with it. `localhost` (Ollama). Client invoices never leave the laptop, which is something a cloud API can't honestly promise.