I agree; I'd say two things in response, though:

1. However good the guess is, it's still just that: a guess. Taking the standard of "evidence in a murder case", the OCR can and probably should be used to point investigators in the right direction so they can go and collect more data, but it should not be considered sufficient as evidence itself.

2. OCR is a relatively constrained solution space - success in those conditions doesn't mean the same level of accuracy can or will be reached outside of that constrained space.

To be clear, though - I'm making a primarily epistemic argument, not one based on utility. There are a lot of areas for which these kind of machine guessing systems are of enormous utility, we just shouldn't confuse what they're doing with actual data collection.

Unless they’re not: https://www.dkriesel.com/en/blog/2013/0802_xerox-workcentres...

I'm not sure about the OCR example, but there are information / sampling theory limits on what can be discerned in an image, based on sampling rate (pixels basically) and optics. Any extrapolation outside these limits is proveably guessing.

Edit - re OCR do you mean e.g. from a picture of a blurred license plate we could rule in or out a subset of possible numbers, depending on how blurred, like a B could be a 8 but not a L? (And sorry if your example is unrelated). This is valid, and unrelated to super resolution, you can do this analysis with Nyquist and point spread functions.