Text Extractor

---
name: pdf-text-extractor
description: "Extract text from PDF files with intelligent chunking and metadata preservation.\
  \ For batch extraction (1K+ PDFs), use pdftotext (poppler) via subprocess \u2014\
  \ see pdf skill Tool Selection table. For single-doc quality, use Codex or PyMuPDF.\
  \ Supports technical documents, standards libraries, research papers, or any PDF\
  \ collection."
type: reference
version: 1.2.0
last_updated: 2026-01-04
category: data
related_skills:
- pdf
- knowledge-base-builder
- semantic-search-setup
- document-inventory
capabilities: []
requires: []
see_also:
- pdf-text-extractor-readability-classification
tags: []
---

# Pdf Text Extractor

## Overview

This skill extracts text from PDF files using PyMuPDF (fitz), with intelligent chunking, page tracking, and metadata preservation. Handles large PDF collections with batch processing and error recovery.

**Tool selection** (see `pdf` skill Tool Selection table for full guidance):
- **Batch (1K+ PDFs)**: pdftotext (poppler) via `subprocess.run(timeout=30)` — 37x faster, reliable timeouts
- **Single doc quality**: OpenAI Codex PDF→Markdown (best understanding)
- **Single doc text**: PyMuPDF (fitz) — fast, good API

> **WARNING (WRK-1277)**: Do NOT use pdfplumber in multiprocessing pools. It hangs in
> kernel D-state on NTFS/NFS mounts — uninterruptible by SIGALRM. Use pdftotext via
> subprocess for all parallel/batch work.

**Note:** The doc-intelligence pipeline uses pdfplumber for single-document extraction.
For bulk extraction across the 1M+ corpus, use pdftotext via subprocess (see
`pdf/pdftotext-poppler` sub-skill for the proven batch pattern).

## Quick Start

**Recommended Approach (with Codex conversion):**
```python
# 1. Convert PDF to markdown first (see pdf skill)
from pdf_skill import pdf_to_markdown_codex

md_path = pdf_to_markdown_codex("document.pdf")

# 2. Process the markdown
with open(md_path) as f:
    markdown = f.read()
    # Work with structured markdown
```

**Direct Extraction (when Codex not needed):**
```python
import fitz  # PyMuPDF

doc = fitz.open("document.pdf")
for page in doc:
    text = page.get_text()
    print(text)
doc.close()
```

## When to Use

- Processing PDF document collections for search indexing
- Extracting text from technical standards and specifications
- Converting PDF libraries to searchable text databases
- Preparing documents for AI/ML processing
- Building knowledge bases from PDF archives

## Related Skills

- `knowledge-base-builder` - Build searchable database from extracted text
- `semantic-search-setup` - Add vector embeddings for AI search
- `document-inventory` - Catalog documents before extraction

## Version History

- **1.3.0** (2026-03-17): WRK-1277 learnings — pdftotext preferred for batch; D-state/NFS/NTFS warnings; fixed duplicate Sub-Skills sections; updated tool selection guidance
- **1.2.0** (2026-01-04): Added OpenAI Codex workflow recommendation as preferred approach; updated Quick Start to show Codex-first workflow; added reference to `pdf` skill for markdown conversion
- **1.1.0** (2026-01-02): Added Quick Start, Execution Checklist, Error Handling, Metrics sections; updated frontmatter with version, category, related_skills
- **1.0.0** (2024-10-15): Initial release with PyMuPDF, batch processing, OCR support, metadata extraction

## Sub-Skills

- [Best Practices](best-practices/SKILL.md)
- [Execution Checklist](execution-checklist/SKILL.md)
- [Error Handling](error-handling/SKILL.md)
- [Metrics](metrics/SKILL.md)
- [Dependencies (+5)](dependencies/SKILL.md)
- [Features](features/SKILL.md)
- [Readability Classification](readability-classification/SKILL.md)
- [Encrypted PDFs (+2)](encrypted-pdfs/SKILL.md)
- [Example Usage](example-usage/SKILL.md)