# grobidOrg/grobid

A machine learning software for extracting information from scholarly documents

Repository: https://github.com/grobidOrg/grobid
Canonical: https://ross.abutalabs.com/products/grobid
Homepage: https://grobid.readthedocs.io
Language: Java
License: Apache-2.0
License Family: permissive
Topics: machine-learning, scientific-articles, pdf, metadata, fulltext, bibliographical-references, hamburger-to-cow, deep-learning, rnn, transformers, crf
Last push: 2026-08-25T10:04:52+00:00

## Health v2 (maintenance only)
Score: 87/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 64, longevity 100
- inputs: {"age_days": 5102, "days_push": 8, "days_rel": 29, "gap_med": 239, "n_releases_24m": 4}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 5105, forks 567 (observed 2026-08-28T04:09:09.796336+00:00)

## What it is
GROBID is a Java machine learning library that extracts, parses, and re-structures raw documents such as PDFs into structured XML/TEI, focused on technical and scientific publications. It provides header extraction, reference parsing, citation context resolution, and full-text document segmentation, with a REST API and Docker deployment.

## Use cases
- extract metadata (title, authors, abstract) from scientific PDFs
- parse bibliographic references from scholarly articles
- convert PDF papers into structured TEI XML
- resolve citation contexts to full references
- segment and structure full text of academic PDFs
- batch process large corpora of scientific publications

## When to choose
- you need high-accuracy extraction of bibliographic data from scholarly PDFs
- you want structured XML/TEI output from unstructured scientific documents
- you need a self-hosted, Docker-deployable PDF parsing service
- you are building scholarly search, citation analysis, or literature review tooling

## When to avoid
- you only need simple text extraction without structure (pdftotext suffices)
- your documents are not technical/scientific publications
- you cannot run a JVM-based service or ML models
- you need extraction from scanned PDFs without OCR preprocessing

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, nlp, pdf, parser, ocr
- domain: machine-learning, pdf, developer-tools
- platform: jvm, cross-platform
- tags: pdf-parsing, tei-xml, scholarly-documents, bibliographic-references, metadata-extraction, deep-learning, crf, transformers, natural-language-processing, docker, web-server

## Member repositories
- grobidOrg/grobid (main) score 87

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:09.796336+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:02:11.512700+00:00, confidence not recorded.
  - readme: https://github.com/grobidOrg/grobid (fetched 2026-08-28T04:09:09.796336+00:00, sha b6e85af9a6d2)
- Data as of 2026-08-30T08:39:29.467469+00:00.