I'm surprised at the low rate every model manages considering the (apparent) ease of the benchmarked document. Can your pipeline produce ground truth as a byproduct ? How do you think open-weight ocr models compare to the one showcased ? I've had good results with glm-ocr on complex documents (complex by their handwriting, pretty easy layouts).

What I like about your solution is the traceability of the information. A scruffy pipeline I used was gemini-flash 3.0 to pdf to notebook-lm (really amateurish work i know), but it yielded tremendeous time gains to extract info from documents (that could be borderline impossible to read for me). However, to trace back the info was obviously very tedious. But from my experience, notebooklm can now manage ocr/htr without a third party. I wonder how competitive your solution might be compared to messy workflows that work -- albeit with efforts -- but let's the researcher be "in contact" with the material.

What I really want is obviously an easy to setup local rag system, with the (very) light model that goes with it ... sweet dream.