Grobid

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GROBID is an open-source machine learning software that extracts and structures information from scholarly PDF documents. It parses bibliographic data, full text, and other metadata, making it production-ready for academic research.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is grobid?

GROBID (Generation Of BIbliographic Data) is an open-source machine learning library designed to extract, parse, and restructure raw PDF documents, particularly technical and scientific publications, into structured XML/TEI encoded formats. Developed since 2008, it offers functionalities like header extraction (title, abstract, authors), reference parsing (with high F1-scores), citation context recognition, and full-text structuring (sections, figures, tables). GROBID also provides PDF coordinates for interactive augmented PDFs, name and affiliation parsing, and consolidation of references using services like biblio-glutton or CrossRef. It includes a comprehensive web service API, Docker images, and supports batch processing, making it suitable for large-scale scientific literature processing. Deployments include ResearchGate, Semantic Scholar, and HAL Research Archive.

Best used for

Ideal for data scientists and professors who need to automate the extraction of bibliographic data, structure full text from PDFs, and resolve citations. Especially valuable for building large-scale structured corpora of scientific articles and integrating into research workflows for efficient information retrieval.

Common actions

extract data

parse documents

structure text

process PDFs

analyze academic papers

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Capabilities

Key features

Header extraction
Reference parsing
Full-text structuring
PDF coordinates
Name parsing
Affiliation parsing
Web service API

Target Audience

data scientistprofessor

Integrations

crossref

Pricing & Plans

Open Source

Free

FAQs

What kind of documents can GROBID process?

GROBID is specifically designed for technical and scientific publications in PDF format. It can extract information from articles, patents, and other scholarly documents, structuring them into XML/TEI encoded formats for further analysis.

Does GROBID use Deep Learning models?

Yes, GROBID uses Deep Learning models via the DeLFT library, alongside feature-engineered CRF models. While CRF models are default for out-of-the-box hardware compatibility, Deep Learning models can be enabled for improved accuracy, especially for reference parsing, if hardware supports it.

Can I use GROBID as a library in my own Java project?

Yes, GROBID provides a Java API and an example project demonstrating its use as a Java library. This allows developers to integrate GROBID's functionalities, such as extracting header metadata and citations, directly into their applications.

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Subcategories trending in Research & Education

Study Assistants Knowledge Management Course Creation Scientific Computing Summarization Language Learning

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Also listed in

This tool also appears in

Data & Analytics › Data Cleaning & Prep AI Agents & Automation › RAG & Document AI Productivity & Business › Workflow Automation

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