I was tired of manual bookkeeping, so I wrote an offline receipt parser with Ollama

Описание и применение

Автономный парсер чеков LedgerLocal использует локальную модель Ollama для извлечения дат, поставщиков и налогов из PDF без облачных вызовов, экономя время и обеспечивая конфиденциальность.

Задача

Freelancers and small businesses manually parse PDFs for totals, VAT, and dates, incurring time and privacy risks.

Результат

LedgerLocal processed 15 freelancer receipts in under 4 minutes, achieving 100% accuracy on tax rates and totals for digital invoices, and was well received as a potential €25 standalone app.

Рабочий процесс

• Upload PDF or text file
• pypdf extracts raw text
• Ollama local inference with phi4-mini:latest
• Model returns JSON with accounting fields
• Pydantic validates and stores in ledger.db
• User reviews/edit via UI or CLI
• Export to CSV or spreadsheet

Доказательства

I looked at commercial receipt scanners. They all have the same two problems: They want $25 to $40 a month...

Источник

The pipeline is simple: Text Extraction, Local Inference, Structured Output, Validation & Storage, UI

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It parsed 15 invoices (PDFs and text receipts) in under 4 minutes.

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I would never pay a monthly subscription for this, but if this was packaged as a standalone desktop app for €25, I'd buy it immediately...

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