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 ...

Ironically, Ollama likely is using Tesseract under the hood. Python library ocrmypdf uses Tesseract too. https://github.com/ocrmypdf/OCRmyPDF

Please add a license file. Thanks!

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-hours.

I really wanted this to be good. Unfortunately it converted a page that contained a table that is usually very hard for converters to properly convert and I got a full page with "! Picture 1:" and nothing else. On top of that, it hung at page 17 of a 25 page document and never resumed.

I’ve been trying to convert a dense 60 page paper document to Markdown today from photos taken on my iPhone. I know this is probably not the best way to do it but it’s still been surprising to find that even the latest cloud models are struggling to process many of the pages. Lots of hallucination and “I can’t see the text” (when the photo is perfectly clear). Lots of retrying different models, switching between LLMs and old fashioned OCR, reading and correcting mistakes myself. It’s still faster than doing the whole transcription manually but I thought the tech was further along.

Does this work with images embedded in the PDF and rasterized images?

This may be a bit of an irrelevant and at best imaginative rant, but there is no shortage of solutions that are mediocre or near perfect for specific use cases out there to parse PDFs. This is a great addition to that.

That said, over the last two years I've come across many use cases to parse PDFs and each has its own requirements (e.g., figuring out titles, removing page numbers, extracting specific sections, etc). And each require a different approach.

My point is, this is awesome, but I wonder if there needs to be a broader push / initiative to stop leveraging PDFs so much when things like HTML, XML, JSON and a million other formats exist. It's a hard undertaking I know, no doubt, but it's not unheard of to drop technologies (e.g., fax) for a better technology.

“Turn images and diagrams into detailed text descriptions.”

I’d just prefer that any images and diagrams are copied over, and rendered into a popular format like markdown.

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-5-nano/mini or Gemini 2.5 Flash are cheap and strong enough to make this practical.

Yeah, it’s a bit like using a rocket launcher on a mosquito, but this is actually very easy to implement and quite flexible and powerfuL. works across almost any format, Markdown is both AI and human friendly, and surprisingly maintainable.

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...

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.

Other tools worthy of mention that help with OCR'ing PDF/Scans to markdown/layout-preserved text:

LLMWhisperer(from Unstract), Docling(IBM), Marker(Surya OCR), Nougat(Facebook Research), Llamaparse.

Similar project used to organize PDFs with Ollama https://github.com/iyaja/llama-fs

careful if you plan on using this. it leverages pymupdf which is AGPL.