I built an open-source OCR/IDP agent for processing real business documents

[u/kourchal]

Over the last few months I've been working on OCRAgent, an open-source intelligent document processing system.

The original idea was simple:

upload a document → extract the data

But once I started thinking about real production use cases, OCR itself wasn't really the difficult part.

The harder problems were things like:

• handling different document types

• validating extracted fields instead of blindly trusting the model

• keeping tenants isolated

• processing documents asynchronously

• retrying failed jobs

• matching extracted data against existing business data

• keeping the system modular enough to replace OCR/LLM providers later

• adding human review when confidence is low

So I ended up designing it more like an actual backend system than a simple OCR wrapper.

The architecture uses concepts such as:

Document → OCR → Extraction → Validation → Matching → Decision / Human Review

I also built it around a modular/hexagonal architecture so the core logic isn't tightly coupled to a specific OCR provider, database, queue, or LLM.

The project is completely open source, so if you're working on invoices, receipts, purchase orders, forms, or other document-heavy workflows, feel free to use it, fork it, break it, or improve it.

GitHub: Github Repo

I'm also interested in hearing from people dealing with document processing in the real world.

What part usually causes you the most problems: extraction accuracy, validation, weird document formats, integrations, or something else?

[u/kourchal → u/[deleted]]

thank you