# deanmalmgren/textract

extract text from any document. no muss. no fuss.

Repository: https://github.com/deanmalmgren/textract
Canonical: https://ross.abutalabs.com/products/textract
Homepage: http://textract.readthedocs.io
Language: HTML
License: MIT
License Family: permissive
Topics: python, natural-language-processing, data-mining, text-mining
Last push: 2026-08-26T03:57:20+00:00

## Health v2 (maintenance only)
Score: 93/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 81, longevity 100
- inputs: {"age_days": 4444, "days_push": 7, "days_rel": 128, "gap_med": 0, "n_releases_24m": 2}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4698, forks 717 (observed 2026-08-28T04:08:57.146636+00:00)

## What it is
A Python library that extracts text from virtually any document format (PDF, DOCX, PPTX, HTML, images, and more) through a simple unified interface. It wraps various parsers and external tools so users can get plain text from documents with a single function call.

## Use cases
- extract text from pdf files in python
- convert word documents to plain text
- parse resumes from pdfs
- bulk extract text from many document formats
- get text out of powerpoint or html files
- preprocess documents for text mining

## When to choose
- you need one uniform API for many document formats
- you want a quick command-line or Python way to pull text from files
- you are building a text-mining or NLP pipeline over mixed document types

## When to avoid
- you need high-fidelity layout or table extraction from complex PDFs
- you want a pure-Python solution without system dependencies like antiword or pdftotext
- the project's maintenance pace is too slow for your production needs

## Facets
- artifact type: library
- maturity: maintenance
- function: parser, pdf, ocr, nlp, file-system
- domain: files, pdf, data-science
- platform: python, cli, cross-platform
- tags: text-extraction, document-parsing, docx, pdf-to-text, natural-language-processing

## Member repositories
- deanmalmgren/textract (main) score 93

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:57.146636+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 2026-08-29T18:19:14.073629+00:00, confidence not recorded.
  - readme: https://github.com/deanmalmgren/textract (fetched 2026-08-28T04:08:57.146636+00:00, sha 6c70badbe4c3)
- Data as of 2026-08-30T08:39:29.467469+00:00.