updated at: June 2025
LLM-Powered Invoice & Receipt Extractor (OSS)
We just open-sourced a language-model-powered extractor for invoices and receipts. It turns messy, unstructured text (from OCR or scanned docs) into clean, structured JSON — complete with field-level confidence scores.
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Additional Information
Struggling to get real receipt/invoice data for your AI models? I built an open-source generator using LLMs (JSON output, no templates)
Link: https://github.com/WellApp-ai/Well/tree/main/ai-receipt-generator
Sample output: https://imgur.com/a/YtFSodj
When you're building AI systems to extract structured data from receipts, invoices, and other financial docs, there's one big bottleneck: Realistic, diverse, high-volume training data.
Most open datasets are:
- Too clean (template-generated)
- Too uniform (Western formats only)
- Not legally usable at scale
So I built this little open-source tool that uses LLMs to generate synthetic receipts in JSON format, fully customizable via prompt + config. No PDFs, no OCR simulation — just structured text output designed for evals, testing, or fine-tuning.
Key features:
- Works with OpenAI, local models, Claude, etc. (LLM-agnostic)
- JSON schema for receipts/invoices, easy to customize
- Faker fallback if you don’t want to hit a model
- Locale-aware: useful for global format simulation
- Configurable weirdness: broken totals, missing fields, typos, etc.
This helped us stress-test our document parser with realistic, non-trivial edge cases that templates couldn’t replicate.
Curious if anyone else here is:
- Generating synthetic data for document AI
- Testing LLM-based extractors or OCR+LLM combos
- Building eval suites for financial AI models
Would love feedback, ideas, or thoughts on how you’d extend this.
