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

说明与用途

LedgerLocal 是一个离线收据解析器，使用本地 Ollama 模型从 PDF 中提取日期、供应商、增值税和分类，消除了手动记账和云隐私问题。

问题

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

结果

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.

工作流

• 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

证据

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

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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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