I tested many open-source and hosted OCR models and Datalab Chandra was the most accurate. It can parse complex layouts, tables, handwriting, and formulas at a fraction of the cost of Claude/Gemini.

Local: https://github.com/datalab-to/chandra Hosted: https://www.datalab.to

Another decent option is GLM OCR. It's slightly less accurate but faster and cheaper.

Local: https://github.com/zai-org/GLM-OCR Hosted: https://docs.z.ai/guides/vlm/glm-ocr

Other models such as PaddleOCR, dots.ocr and DeepSeek OCR performed significantly worse.

I've seen papers using fine-tuned small local vision models to transcribe math into latex. Personally I tried Claude Opus & Claude Code directly to do this. It works (to my surprise) but AI sometimes take guesses that are mathematically right but deviates with my original writing. There is no 100% correct method though.

> take guesses that are mathematically right but deviates with my original writing.

Its rather interesting if it's correcting a mistake or picking a correct alternative.

How accurate do you think it is overall?

you could try using a local vision model, like Mage-VL from microsoft. Its only a 5b model so its quite small for the capability it has.

Most LLMs can do a pretty good job of this out of the box.

If you have a coding agent available, ask it to try transcribing a few of the PDFs.