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.
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.