Built a 40k-line Synthetic Data Engine to solve Small Text VLM/OCR. Now looking for the next big challenge (Acqui-hire/Collab)

Hey Indie Hackers,

I'm a solo founder based in Europe, building tools at the intersection of computer vision and enterprise AI. After months of heads-down development.

I'm excited to launch OCR Producer.

GitHub: https://github.com/alrowilde/ocr-producer

Check the GitHub link for side-by-side visual comparisons: You'll see how the engine handles the character-level degradation and complicated layouts that traditional augmentations simply can't mimic.

To be honest, I created this project primarily for OCR tasks, but I designed the different modules independently, the modular design makes it easy to extend for VLM-specific tasks.

A synthetic data engine designed to solve some of the toughest pain points in real-world Document AI.

The Pain Point: Small Text Is Critical, But Always Under-Represented In enterprise OCR (invoices, medical forms, logistics labels, financial reports), there's a nasty imbalance that quietly kills model performance:

Most fields are long text: Company names, addresses, descriptions – easy to spot, plenty of examples in every document.
But the critical fields are tiny: Quantities (e.g., "1", "*" or "#"), unit prices, tax rates, counts in small fonts, many critical categories consist of single characters or very short text, often printed in 6-12pt font, sometimes with noise or occlusion.

In real datasets, small text instances are ridiculously rare – maybe 5-10 per document vs 50+ long fields. When you train a model (whether traditional OCR like PaddleOCR/Tesseract or fine-tuning a VLM), the model learns to nail the easy stuff and consistently fails on the stuff that actually matters most.

How OCR Producer Solves It
I built OCR Producer as an on-premise synthetic data engine that lets you generate unlimited, high-fidelity training samples specifically targeted at these failures.

Key features that address the small text problem:
Realistic business layouts: Invoices, tables, medical forms, multi-column reports – with merged cells, noise, blur, and distorted characters.

On-premise & privacy-safe: Runs 100% locally. No uploading sensitive templates or data to the cloud.
Bonus: Strong CJK support: If you've ever tried OCR on dense East Asian forms, you know the pain. This engine was stress-tested there first.

The results
You can balance your dataset overnight: turn 100 real invoices into 10,000+ synthetic ones with perfect small-text representation, fully labeled.
Instead of waiting for data engineers to process data every time, the data production process can be transformed into a parallel process.

Quick Results I've Seen
In my own tests (using open-source PaddleOCR baselines):
Small text detection becomes far more stable. Overall detection of key business fields improves significantly, though limited layout diversity can still impose some constraints.
Overall document precision improved by 10-15% compared to the baseline PaddleOCR model in dense-text invoice scenarios.

If you're building in FinTech, Healthcare, Logistics, or any regulated space where VLM/OCR precision actually moves the bottom line, I'd love to chat.

I've built this to a point where the core engine is robust (40k+ lines of code, solo-developed) and the architecture allows for rapid debugging and error tracing.

Now, I’m looking to bring this technology into a larger ecosystem. Whether it’s a strategic partnership or joining a team to lead their Document AI efforts, I’m open to discussing how this engine can be integrated into your production pipeline.

Thanks for reading – excited to be part of this community!

If you have any questions, please reach out to me at [email protected]