It’s OCR because the wrong characters are being recognized. This is not about fixing spelling or punctuation mistakes present in the source image, it’s that errors are being introduced, due to a lack of accuracy of this OCR with regard to punctuation and typography. The punctuation errors are not different in principle from the case of the OCR producing a misspelled word that wasn’t misspelled in the image being OCRed.

A subsequent cleanup pass that fixes grammar/spelling errors, as you propose, wouldn’t be appropriate when the goal is to faithfully reproduce the original text.

And specifically for the “white circle” character, it would be difficult to correctly infer the original ordinal markers after the fact. I myself could only do so by inspecting the original image, i.e. by having my brain redo the OCR.

> A subsequent cleanup pass that fixes grammar/spelling errors, as you propose, wouldn’t be appropriate when the goal is to faithfully reproduce the original text

I suppose that depends on why it's wrong. Did the model accurately read a real typo in the image or did it incorrectly decipher a character? If a spelling & grammar pass fixes the latter, isn't it valid?

Not unrelated - OneNote 'copy text from image' has started producing lots of incorrect OCR results, but they're all non-words.

For example, from a clear image of a printed page (in a standard font), it will give me 'cornprising' instead of 'comprising'; 'niatter' instead of 'matter'. Excepting the spell-check underline they'd be hard to spot as with relatively tight kerning all the errors look like the originals.

I'm surprised as 1) I've not had these sorts of errors before, 2) they're not words, and words must be heavily weighted for in the OCR engine (I'd have thought).