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Llama-Scan: Convert PDFs to Text W Local LLMs

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Re: Llama-Scan: Convert PDFs to Text W Local LLMs

#42

I’ve done a similar PDF → Markdown workflow. For each page: - Extract text as usual. - Capture the whole page as an image (~200 DPI). - Optionally extract images/graphs within the page and include them in the same LLM call. - Optionally add a bit of context from neighboring pages. Then wrap everything with a clear prompt (structured output + how you want graphs handled), and you’re set. At this point, models like GPT…

>are cheap and strong enough to make this practical.

It all depends on the scale you need them, with the API it's easy to generate millions of tokens without thinking.

Re: Llama-Scan: Convert PDFs to Text W Local LLMs

#43

Sub-2010 level OCR using LLM. It is hype-compatible so it is good. It is AI so it is good. It is blockchain so it is good. It is cloud so it is good. It is virtual so it is good. It is UML so it is good. It is RPN so it is good. It is a steam engine so it is good. Yawn...

>Sub-2010 level OCR

It's not.

Re: Llama-Scan: Convert PDFs to Text W Local LLMs

#46

Looking at the code, this converts PDF pages to images, then transcribes each image. I might have expected a pdftotext post-processor. The complexity of PDF I guess ...

Saw this tweet the other day that helped me understand just how crazy PDF parsing can be https://threadreaderapp.com/thread/1955355127818358929.html

There are a few other reasons why PDF parsing is Hell! > https://unstract.com/blog/pdf-hell-and-practical-rag-applica...

Re: Llama-Scan: Convert PDFs to Text W Local LLMs

#48

Give the nanonets-ocr-s model a try. It’s a fine tune of Qwen 2.5 vl which I’ve had good success with for markdown and latex with image captioning. It uses a simple tagging scheme for page numbers, captions and tables.

I desperately wanted Qwen vl to work but it just unleashes rambling hallucinations off basic screencaps. going to try nanonet!

Re: Llama-Scan: Convert PDFs to Text W Local LLMs

#49

By 1990 Omnipage 3 and its successors were 'good enough' and with their compact dictionaries and letter form recognition were miracles of their time at ~300MB installed. In 2025 LLMs can 'fake it' using Trilobites of memory and Petaflops. It's funny actually, like a supercomputer being emulated in real time on a really fast Jacquard loom. By 2027 even simple hand held calculator addition will be billed in kilowatt-ho…

If you think 1990's ocr- even 2000's OCR is remotely as good as modern OCR... I`v3 g0ta bnedge to sell.

I had an on-screen OCR app on my Amiga in the early 90s that was amazing, so long as the captured text image used a system font. Avoiding all the mess of reality like optics, perspective, sensors and physics and it could be basically perfect.
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