Comment by mdp2021
6 days ago
I tried it! (How very distracted of us not to have tried immediately.) And it works, mostly... I used a public LLM.
The sentence:
> ABG News was founded in 194S after action from the PCC , _ , demanciing pluralist progress 8 vears ear!ier
is corrected as follows, initially:
> ABG News was founded in 1945 after action from the FCC, demanding pluralist progress 8 years earlier
and as you can see it already corrects a number of trivial and non-trivial OCR errors, including recognizing (explicitly in the full response) that it should be the "FCC" to "demand pluralist progress" (not to mention, all of the wrong characters breaking unambiguous words and the extra punctuation).
After a second request, to review its output to "see if the work is complete", it sees that 'ABG', which it was reluctant to correct because of ambiguities ("Australian Broadcasting Corporation", "Autonomous Bougainville Government" etc.), should actually be 'ABC' because of several hints from the rest of the sentence.
As you can see - proof of concept - it works. The one error it (the one I tried) cannot see is that of '8' instead of '3': it knows (it says in a full response) that the FFC acted in 1942, and ABC was created in 1945, but it does not compute the difference internally nor catch the hint that '8' and '3' are graphically close. Maybe one LLM with "explicit reasoning" could do even better and catch also that.
I'm saying it's moot because, if you're just flagging things for review, there's already a more direct and reliable way to do that. The OCR classifier itself outputs a confidence score. The naieve way of just checking that confidence score will work. The OCR classifier has less overall information than an LLM, but the information it has is much more relevant to the task it's doing.
When I have some time in front of a computer, I'll try a side-by side comparison with some actual images.