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Comment by xz18r | Hacker News Reader

Why exactly does this need to be AI? OCR was a thing way before the boom and works pretty fine, usually. Seems like overkill.

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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?

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

Search? AI.

Linear regression? AI.

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.

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.

Know of any classic OCR tools that can reliably extract tabular data from scrappy PDFs? I've been hunting for a dependable option for that for years.

If you're looking for better accuracy and table layout preservation, give LLMWhisperer and Docling a try! Both keep tables tidy with a Markdown-like structure.

I've not found one either. I did this at a very large scale recently and ended up just using pdfplumber. I did POC Table Transformer but the cost was too high at my scale--there are probably better options now anyway. Most seem to focus on structure detection and then use traditional OCR for the actual content extraction.

It's a very hard space in the long tail, like tables that span pages or tables with complex internal structures. I went into it thinking "eh how hard can tables be?". Very hard. Thankfully it's a pretty active research area.

Look at pages 18-20 of the technical report. I don't know of any non-AI OCR that can do as good a job as that.

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