3 ms·

I think it's interesting how we call this AI, because neural networks have been used for OCR for literally decades at this point. Where does "neural networks"

I think it's interesting how we call this AI, because neural networks have been used for OCR for literally decades at this point. Where does "neural networks" stop and "AI" begin?

remram 2y ago
I'm in academia, my understanding is that all statistical methods are AI now. Search? AI. Linear regression? AI.
adrian_b 2y ago
In my opinion, the use of AI/ML/neural networks for the recognition of individual letters or of ligatures is perfectly valid. However for OCR, I do not want any kind of AI that attempts to use a context bigger than that, i.e. attempting to recognize words, phrases, sentences. I find much more acceptable an OCR tool that fails to recognize all the characters, marking some as unknown, than one tool that returns even a single wrongly guessed word. While there may be some kinds of documents where an LLM may guess any missing content with reasonable accuracy, all the documents that I would want to process with OCR, i.e. mostly old or very old books, have content where one could guess successfully something only with a very thorough knowledge of the other writings of the same author, of the subject matter and of the characteristics of the language that was used in that historical period. A LLM trained very specifically for the text author, text subject and contemporaneous writings might have chances of success, but none of the available LLMs is like this and it is much cheaper anyway to just use an OCR tool that does not attempt to make contextual guesses and then resolve any unreadable characters by humans.
- dredmorbius 2y ago
  Though it's worth mentioning that inference from context (which an LLM OCR tool is presumed to do) is precisely what a human transcriptionist does. This can result in "nonfaithful" reproduction where, say, a tyop in an original document is corrected, consciously or unconsciously, when transcribing. Taking into account both the local context (on a given page) and the larger context of a work (within a subject area, collection, etc.) I'd expect an AI-based OCR tool would behave similarly. For archival / academic work, what would be nifty would be a tool which would note the original text image context, a certainty probability score, and possible alternative transcriptions in cases of ambiguity. It's a nice idea to get humans in the loop, but realistically this simply won't always be possible, and it's helpful to think of what next-best approaches might be. It wouldn't surprise me if AIs turned out to be more generally reliable in such cases, though I would also expect some wild mis-fires and fumbles along the way.

I think it's interesting how we call this AI, because neural networks have been used for OCR for literally decades at this point. Where does "neural networks" stop and "AI" begin?

remram 2y ago

I'm in academia, my understanding is that all statistical methods are AI now. Search? AI. Linear regression? AI.

adrian_b 2y ago

In my opinion, the use of AI/ML/neural networks for the recognition of individual letters or of ligatures is perfectly valid. However for OCR, I do not want any kind of AI that attempts to use a context bigger than that, i.e. attempting to recognize words, phrases, sentences. I find much more acceptable an OCR tool that fails to recognize all the characters, marking some as unknown, than one tool that returns even a single wrongly guessed word. While there may be some kinds of documents where an LLM may guess any missing content with reasonable accuracy, all the documents that I would want to process with OCR, i.e. mostly old or very old books, have content where one could guess successfully something only with a very thorough knowledge of the other writings of the same author, of the subject matter and of the characteristics of the language that was used in that historical period. A LLM trained very specifically for the text author, text subject and contemporaneous writings might have chances of success, but none of the available LLMs is like this and it is much cheaper anyway to just use an OCR tool that does not attempt to make contextual guesses and then resolve any unreadable characters by humans.

dredmorbius 2y ago
Though it's worth mentioning that inference from context (which an LLM OCR tool is presumed to do) is precisely what a human transcriptionist does. This can result in "nonfaithful" reproduction where, say, a tyop in an original document is corrected, consciously or unconsciously, when transcribing. Taking into account both the local context (on a given page) and the larger context of a work (within a subject area, collection, etc.) I'd expect an AI-based OCR tool would behave similarly. For archival / academic work, what would be nifty would be a tool which would note the original text image context, a certainty probability score, and possible alternative transcriptions in cases of ambiguity. It's a nice idea to get humans in the loop, but realistically this simply won't always be possible, and it's helpful to think of what next-best approaches might be. It wouldn't surprise me if AIs turned out to be more generally reliable in such cases, though I would also expect some wild mis-fires and fumbles along the way.