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It's not. OCR is not what the vision model is doing here. We're used to using OCR as a verb but it's more accurate to say the model "visioned" it.

Also, some models still do OCR and it's usually way more expensive that way.

OCR means optical character recognition. The terms do not require a direct transcription, but that is mostly what OCR meant in the past. If you’re using an LLM’s vision capability to pass in text and the LLM actually understands it, then I would say that it recognized the characters, hence OCR seems okay to use.

But characters only exist when we ask the model about this and it does it best to do this projection if asked. Vision model are richer than that. It "understands" visually a document. If it was only about characters, then there will be no way it beats the traditional pipelines of image->text->extractions or obtain the kind of results we see in this article. Vision models are more than characters recognition and OCR term don't do it justice IMHO.

So if I OCR a document, edit it, and print it, OCR didn't happen?