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Really looking for something we can run locally in terms of OCR LLM, I think a lot of people doing a lot of OCR and document extraction aren’t looking to upload every file into the cloud and the use is more narrow than typing into a chatbot.

While Gemini is nice, it would be nice to have a pipeline that works locally on a reasonably RAM’d unified memory Mac or Framework AMD board.

eithed · 6h ago

OCRs don't hallucinate outputs = if it says "212.99mm" on architecture diagram it doesn't suddenly turn into "2413m" on the other end, because LLM thought this feels better. I remember reading on HN where that was happening in a such case (but sadly my google foo fails me to find a link)

strangecasts · 6h ago

The case you might be thinking of is the JBIG2 implementation bug [1, 2] in Xerox photocopiers where the pattern-matching would incorrectly treat certain characters as interchangeable, leading to numbers getting rewritten in spreadsheets.

[1] https://www.bbc.com/news/technology-23588202

[2] https://www.dkriesel.com/en/blog/2013/0810_xerox_investigati...

eithed · 5h ago

That's exactly it! Thank you!

endymion-light · 7h ago

I don't mind people doing blog-posts advertising they're own companies - but I feel like i'd like a little bit more substance within this topic. It is interesting in a way, I find I turn to things like gemini 2.5 within simple OCR/NLP and now more substantial image editing than specific models.

I think that's more because of the current state of the industry, a lot of those models are either internal, paywall locked or annoying to use. I don't want to waste effort in trying to sign up for a 4 week trail of X service to perform a one off task.

Unfortunately, this post didn't really elucidate or go into an interesting topic within this space.

I'm not expecting a research paper, but it would be great to get some stats, graphs, examples and meat on the bones. I opened this up expecting some actual examples of problems within OCR & NLP and showing how X multi-modal model solves them.

universesquid · 5h ago

cool thanks for that comment, I might update this in a couple weeks time since it seems to interest people but general feedback that it's too shallow. Wanted to give some high level intuition I gained after working on document processing for a while now as many people are still surprised that e.g. layouts aren't a real problem anymore but will take the hint that hn is a crowd that wants more depth! :)

behnamoh · 7h ago

This is a nothing burger blog post that likely made it to the front page because it mentions "LLM" in the title. Worse yet, it's an ad actually.

OtherShrezzing · 7h ago

The first thing I do on HN posts with lots of upvotes and few comments is scroll to the bottom and check if the closing paragraph has a link to some saas product. If it does, I close the tab.

thaeli · 7h ago

Ironically, this check would be a pretty good use for a LLM.

WesleyLivesay · 7h ago

You beat me to this comment, but you are absolutely correct.

Tractor8626 · 7h ago

OCR doesn't have prompt injection problem

mattigames · 7h ago

It's only prompt injection if it comes from state sponsored hackers, otherwise it's just surprise prompt augmentation.

tiahura · 7h ago

"I still believe that processing documents will be a solved problem in a couple years time."

Current 80/20-rule-ignoring AI dogma in a nutshell.

tovej · 7h ago

Are LLMs not NLP? They process natural language, no?

And I assume the multimodal tools still use OCR for text extraction, or am I missing something?

My understanding is that they're still doing OCR+NLP, just differently than traditional approaches.

universesquid · 5h ago

1.) technically yes, most models used for that task are NLP but not LLMs in the modern sense though 2.) Actually they don't. Multimodal LLMs parse PDFs by taking multiple screenshots on each page.

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