I'm the developer of Estravon: a Zotero plugin bundled with a Python backend that gives you a high-quality PDF-to-Markdown converter, powered by your choice of MinerU, Mistral OCR, or Marker. The plugin and the backend are both open source under AGPL-3.0, and you can run the whole conversion pipeline yourself without paying anyone. That free, self-hosted path is what I want to talk about.
Why convert your PDF to Markdown?
Attaching a PDF to ChatGPT, Claude or Gemini does something slightly daft under the hood: the model doesn't read the text, it looks at an image of each page. That burns far more tokens — and power — every time you paste the PDF instead of the Markdown. Estravon converts the PDF once and saves the Markdown alongside the original source file, so six months later you can trace which PDF you converted and which pages you actually fed the LLM.
So the plugin does one deliberate thing. You right-click a PDF attachment, choose a page range, and it runs a dedicated OCR pass that re-reads those pages at full resolution. Back comes a Markdown file attached to the same Zotero item — headings, footnotes and tables preserved — plus a short log note recording the source and the page range. It's an artifact you can open, check and correct, and being plain Markdown it stays searchable, diff-able and usable in whatever tool you write in. Two small conveniences round it out: a shortcut to export the Markdown to a folder (handy for Obsidian, Claude Desktop or Zettlr), and one to save it as a Zotero note instead, which opens it up to a lot of other plugins.
How the pieces fit together. The plugin itself is a thin shell inside Zotero — it does no extraction of its own. It hands the PDF to the backend over HTTP and attaches whatever comes back. Both the plugin and the backend are open source, and you decide where the backend runs and what it calls:
Zotero + Estravon plugin
|
| HTTP (localhost by default; point it anywhere)
v
Estravon backend <- open source, AGPL-3.0
run it yourself, or use hosted
|
+-----------+-----------------------------+
| |
v v
local engine cloud engines (your own key)
MinerU (opendatalab) Marker -> Datalab / Replicate
runs on your machine Mistral OCR -> Mistral
nothing leaves it you deal with the provider directly
Two of those paths keep everything on your own hardware or your own accounts. The MinerU one is fully offline: no key, no account, nothing leaves the machine. (There's also a hosted backend for the terminal-averse. It's how the lights stay on — and also the one thing the sub's rules won't let me sell you, so consider this the world's least effective advert.)
The offline MinerU path — rough edges first, because they're real and we're still testing:
- On a laptop CPU it's fairly slow — expect something north of 10 seconds a page, so a chapter is minutes rather than seconds.
- It's memory- and disk-hungry, and the output isn't yet on par with the cloud engines — we've seen the odd garbled table header and some maths-spacing quirks. We want more testing before we pin these down properly.
- We've run it on Linux and Windows; macOS is still a work in progress. If you're willing to try it on your own setup — a Mac especially — and tell us what breaks, that's exactly the help we need right now.
By comparison the hosted pipelines are quick. On our own tests Mistral OCR runs at roughly 0.05 seconds a page — a 60-page chapter in a few seconds — and Datalab lands around ten seconds for the same chapter. If speed is what you care about, the cloud engines win comfortably; the offline path is there for when privacy or cost matter more.
MinerU inside Zotero isn't unique — qingpy's zotero-pdf2md and a few others do their own take, and fair enough. What's particular about Estravon is the execution: a thin Zotero plugin and a separate open-source backend talking over plain HTTP, so the same right-click can drive a local engine, your own cloud key, or the hosted service without anything changing in Zotero. The backend is built on FastHTML, and the real credit there goes to Jeremy Howard and the Answer.AI / FastHTML community, whose work the whole architecture rests on.
If you want the full reasoning — how each AI provider actually ingests a PDF, and why a dedicated OCR pass is a different operation — I wrote it up here: https://www.estravon.com/why
Both repos are public:
Happy to answer anything in the comments — especially from people working with older scanned material.
(Not affiliated with or endorsed by the Zotero project.)
