Cross Validated likes to promote proper scoring rules in "classification" problems. That is, get accurate probability predictions. Then make the classifications, taking into account the cost of misclassifications. This works well for a situation like medical diagnosis, where, as Frank Harrell argues, the physician would be most interested in the probability. Even the task of spam email detection, which a computer handles automatically by sticking emails in discrete buckets, has a role for a probability calibration.
One place where I cannot see a role for a well-tuned probability, however, is optical character recognition. We stick a printed document into a scanner, and it turns the images of the letters into text of the letters. As far as I can tell, all we would care about is how accurate the resulting text is.
What would be the role of a proper scoring rule that seeks the true probabilities if my lone goal is to train an accurate classifier?
Does optical character recognition do the statistics and decision in one step?
Why would I even want to use a proper scoring rule as the loss function if I'm just going to assess the model based on its ability to assign the highest class probability to the correct character?