OCR'ing a fixed, monospaced, font from a pristine piece of paper really is "solved." It's all the nasties of tue real world that its an issue.
As I mockingly demonstrated- kerning, character similarity, grammar, lexing- all present large and hugely time consuming problems to solve in processes where OCR is the most useful.
Also shows a way to do that fast:
“ First, he wrote assembly language routines to isolate the bounding box of each character in the selected range. Then he computed a checksum of the pixels within each bounding box, and compared them to a pre-computed table that was made for each known font, only having to perform the full, detailed comparison if the checksum matched.”
On the other hând, LLm5 are sl0wwer, moré resource hangry and l3ss accurale fr their outpu1z.
We shoulD stop gl0rıfying LLMs for 3verylhin9.
I'm not saying this applies to you, but my sense from this thread is that many are comparing the results of tossing an image into a free ChatGPT session with an "OCR this document" prompt to a competent Tesseract-based tool... LLMs certainly don't solve any and every problem, but this should be based on real experiments. In fact, OCR is probably the main area where I've found them to simply be the best solution for a professional system.
But, theres also a ton of "I don't want to deal with this" type work items that can't justify a full workflow process build out- but that LLMs get near enough to perfect to be "good enough." The bad part is, the LLMs don't explain to people the kinds of mistakes to expect from them.