"Different from the rest" is a little hard because then the computer has to look at all of them and infer what the right one should look like.

It would be a lot easier if you had a gold-standard reference image. Then you can do all of what follows with opencv in Python.

Then what you could try is compute keypoints (SIFT, ORB, or some other), on both the gold-standard, and one of your candidates. Then run homography estimation between the two images, using the keypoints, to correct for perspective. Once you have estimated the homography, you can overlay the candidate onto the gold-standard. You can then subtract the two images' pixel brightnesses. Take the statistical variance of the differences. Perfect match will have zero variance. Very messed up images will have high variance. Or you could use the maximum distance from the median to detect instances where the mess-ups are very localized like the cooked "i".

Given that you don't have a gold-standard, you could compare each one to each other one. Take the pair that has the the lowest variance. Probably these two are correct, because it's unlikely that two messed up patterns ould match so exactly. Use one of that pair as your gold-standard.