GROBID for a scientific archive
Academic paper parsing: metadata, citations and references
- Video memory
- 4 GB
- Setup
- ~3 min
- Access
- port 8070
- Billing
- hourly, in BRL
GROBID reads a paper PDF and returns structured XML: title, authors, affiliations, abstract, sections and the reference list split entry by entry. It is the foundation of any automated systematic review.
GROBID extracts structured data from scientific papers (TEI XML): header, authors, abstract, sections, formulas, citations and references. Industry standard (CERN, Inria, Allen AI).
What it is for
- Systematic reviews: extract and cross-reference hundreds of papers
- Build a citation base for bibliometric analysis
- Feed an academic RAG with pre-sectioned text
- Process an internal archive without sending PDFs outside
How to deploy
- Create your account and add balance (card or Pix, no subscription).
- In the console, pick the GROBID template and a machine — the console hides the ones that do not meet the requirement.
- In about 3 minutes the setup finishes and the access address shows up in the panel, on port 8070.
Done? Just destroy the machine and billing stops with it. No contract, no minimum commitment.
FAQ
Does it need a GPU?
It helps for the deep-learning models, but volume is what matters. An entry-level card already speeds batches up considerably.
What format does it return?
TEI XML, the standard in digital humanities and easy to convert to JSON.
Can it run in batch?
Yes, the service exposes an HTTP API and handles queues of thousands of PDFs.