Off the top of my head: for a lot of OCR tasks, it’s kind of worse for the model to be smart. I don’t want my OCR to make stuff up or answer questions — I want to to recognize what is actually on the page.
replySometimes what is on the page is ambiguous. Imagine a scan where the dot over the i is missing in a word like "this". What's on the page is "thls" but to transcribe it that way would be an error outside of forensic contexts.
replyI am reminded it's basically impossible to read cursive writing in a language you don't know even if it's the same alphabet.
Interesting. Won't stuff like entity extraction suffer? Especially in multilingual use cases. My worry is that a smaller model might not realize some text is actually a persons name because it is very unusual.
replyThe model does not need to be that smart to understand that a name it does not know that starts with a capital letter is a the name of a place or a person. It does not need to be aware of whom this refers to, it just needs to transcribe it.
replyAlso, there are generalist models that have enough of a grasp of a dozen or so languages that fit comfortably in 7B parameters. Like the older Mistral, which had the best multi-lingual support at the time, but newer models around that size are probably good candidates. I am not surprised that a multilingual specialised model can fit in 8B or so.
No. Gemini is clearly the leader across the board: https://www.ocrarena.ai/leaderboard
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