This might seem like a quibble, but once you dive a little deeper into it, you realise that there's enormous latitude and subjectivity in the way you do that interpretation.

What's even crazier is that this didn't come with digital photography. Analogue film photography has the same problem. The silver on the film doesn't become an image until it's interpreted by someone in the darkroom.

There is no such thing as an objective photograph. It's always a subjective interpretation of an ambiguous record.

With ML-enhanced photos, you might have a distanced face that is "enhanced" by the model, to become a face that wasn't there. Or a fingerprint, a birthmark, a mole, etc.

The nice thing about this was that you could hand the E-6 off to a magazine and end up with a photograph printed in the magazine that was very close to the original film. Any color shifts or changes in contrast you could see just with your eyes. You could drop the film in a scanner and visually confirm that the scan looks identical to the original. (You cannot do this with C-41.)

This was not used for forensic photography, though. The point of using E-6 was for the photographer to make artistic decisions and capture them on film, so they can get back to taking photos. My understanding is that crime scene photography was largely C-41, once it was relatively cheap.

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.

But the issue manifests as characters being incorrectly identified because of an algo t

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.

I don't. Everyone knows this already and it seems like a lot of people are just saying it over and over to look clever.