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anystyle-api

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Citation reference parser using machine learning

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# AnyStyle API Guide

## Overview

AnyStyle is a fast and smart citation reference parser that uses machine learning (specifically conditional random fields, CRFs) to extract structured bibliographic data from unformatted citation strings. It can parse raw reference text into structured fields such as author, title, journal, volume, pages, year, and DOI, handling the enormous variety of citation formats found in academic literature.

The AnyStyle service provides both a web interface and an API endpoint for programmatic citation parsing. Unlike rule-based parsers that rely on specific citation style templates, AnyStyle uses a trained machine learning model that generalizes across citation formats, making it effective for parsing references from diverse disciplines and publication traditions where citation styles vary widely.

Researchers, librarians, digital humanists, and research software developers use AnyStyle to extract structured references from PDF documents, legacy bibliographies, dissertation reference lists, and scanned documents. It is particularly valuable for building citation networks, enriching bibliographic databases, migrating references between management tools, and processing large volumes of unstructured citation data that would be impractical to parse manually.

## Authentication

No authentication required. The AnyStyle web service is freely accessible without any API key, token, or registration. The service can be used via the web interface at https://anystyle.io/ or through its API endpoint. For heavy usage or private deployments, AnyStyle is also available as an open-source Ruby gem that can be installed locally.
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