MonkeyOCR

MonkeyOCR turns documents into structured data: it reads a PDF or a scan and pulls out the text, the tables, and the math formulas in the correct reading order. If you've ever needed clean data out of messy PDFs, that's the job. It's a compact model, 1.2B or 3B parameters, that punches well above its size, beating heavier open models and even some commercial APIs on document benchmarks. The code is Apache 2.0.

You run it yourself on a GPU. A quantized version fits on an 8GB card like a 4060, and the full model is comfortable on a 3090, 4090, or A100, working through roughly one page a second depending on hardware. That's the trade: no per-page API fees, but you need a GPU and the willingness to set up a Python inference stack.

For pulling structure out of documents, this is one of the strongest open options right now, clearly ahead of Marker and Docling on benchmarks and competitive with PaddleOCR. Solo or research use: excellent, and free. Teams processing documents at volume: the economics beat a cloud OCR API quickly if you already have GPUs.

Here's the catch, and it matters. The authors state the model is intended for academic research and non-commercial use only, even though the code carries an Apache license. If you're shipping this inside a commercial product, read the model terms carefully before you build on it.