For exemple in the case of OCR, a person with myopia will usually be able to make up letters and words even without his glasses based on his expectation (similar to vlm training) of seeing letters and words in, say, a sign. He might not see them all clearly and do some errors but might recognize some letters easily and make up the rest based on context, words recognition, etc. Basically experience.
I also have a funny anecdote about my partner, which has sever myopia, who once found herself outside her house without her glasses on, and saw something on the grass right in front. She told her then brother in law "look, a squirrel" Only for the "squirrel" to take off while shouting its typical caws. It was a crow. This is typical of VLM's hallucinations.
So unless I missed something but as far as I am concerned, they are optimized for benchmarks.
So while I enjoy gen AI, image-to-text is highly subpart.
At least that's what I would do. Perhaps the script would be a "specialist model" in a sense.
Sometimes if you upload an image to ChatGPT and ask for OCR it will run Python code that executes Tesseract, but that's effectively a bug: GPT-4 vision works much better than that, and it will use GPT-4 vision if you tell it "don't use Python" or similar.
[1] https://huggingface.co/spaces/opencompass/open_vlm_leaderboa...