Known font, known range of possible characters (almost certainly ascii), and probably several blurred characters in there that we know (like the t in attachment). If the blur is differentiable per-character, it's probably reversible.

Even without automation. Just make a list of blurred alphanumerics, then match every blurred character in the picture against the character list. with some patience probably doable in a single day

That’s called generative recognition, and it’s the best way to do it (probably can be formally proved under some noise model assumptions) if you have a good model of the data and enough computational resources.

What tools do people use for stuff like this? A python library? C++?

Probably the most time efficient way to do it tbh.

You don't need ml. If you know the font and blurring algorithm (or a close approximation), you blur all letters of the font at that size and compare output.

Most of the time, blurring is down with a Gaussian blur and this is, in theory, reversible. In practice what is actually done is round_to_fraction_over_256(gaussian_blur(point, image, …)) so there is an error in the unblurring process. But this error is often not insurmountable, especially with extra information.

Actually, if you can identify the exact font and exact location of each letter (as is in this case) it doesn't matter what kind of blur it is.

If you assume the photo was made with one of the consumer applications, there are only so many popular bluring algorithms. You can brute force it quite easily by testing each character and each type of blur until you get exact match (and in many cases even inexact will suffice).

Not at all, I can already make out the characters and recognized quickly that all but the last 4 blurred characters are hexadecimal and the last appear to be [a-z][A-Z]

It's blurred lightly enough for me to be able to tell at least some characters just with my eyes.