For non-table documents a fine tuned yolov8 + tesseract with _good_ image pre-processing has basically a zero percent error rate on monolingual texts. I say basically because the training data has worse labels than what the multi-model system gives out in the cases that I double checked manually.

But no one reads the manual on tesseract and everyone ends up feeding it garbage, with predictable results.

Tables are an open research problem.

We started training a custom version of this model: https://arxiv.org/pdf/2309.14962 but there wasn't the business case since the bert search model dealt well enough with the word soup that came out of easy ocr. If you're interested drop a line. I'd love to get a model like that trained since it's very low hanging fruit that no one has done right.