OCR libraries have undoubtedly improved but LLMs are using the same open source libraries and tools available to anyone… there’s few cases where sending the work through general models is worth it for text conversion. Employees just needed a front end to upload, run something like tesseract behind the scenes, and spit out the result. It’s an egregiously stupid use of resources.
have undoubtedly improved but LLMs are using the same open source libraries and tools available to anyone…
I read a surprising article on Lemmy just a week ago that explained that that is not how LLM’s do OCR. LLM’s convert images into tokens and then treat them like text input. I can’t see how it works but it does. It’s why they are better than classic OCR neural nets but at the trade off of enormously larger computation cost.
OCR libraries have undoubtedly improved but LLMs are using the same open source libraries and tools available to anyone… there’s few cases where sending the work through general models is worth it for text conversion. Employees just needed a front end to upload, run something like tesseract behind the scenes, and spit out the result. It’s an egregiously stupid use of resources.
I read a surprising article on Lemmy just a week ago that explained that that is not how LLM’s do OCR. LLM’s convert images into tokens and then treat them like text input. I can’t see how it works but it does. It’s why they are better than classic OCR neural nets but at the trade off of enormously larger computation cost.
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