Extract text and content from PDFs, scanned documents, and office files. Invoke when user asks to "read this PDF", "extract text from document", "OCR this scanned file", "convert PDF to text", "read DOCX content", or "parse PPTX slides". Uses pymupdf for local PDFs, marker-pdf for OCR, and python-docx for Word files.
For DOCX: use python-docx (parses actual document structure, far better than OCR).
For PPTX: see the powerpoint skill (uses python-pptx with full slide/notes support).
This skill covers PDFs and scanned documents.
If the document has a URL, always try web_extract first:
web_extract(urls=["https://arxiv.org/pdf/2402.03300"])
web_extract(urls=["https://example.com/report.pdf"])
This handles PDF-to-markdown conversion via Firecrawl with no local dependencies.
Only use local extraction when: the file is local, web_extract fails, or you need batch processing.
| Feature |
|---|
| pymupdf (~25MB) |
|---|
| marker-pdf (~3-5GB) |
|---|
| Text-based PDF | ✅ | ✅ |
| Scanned PDF (OCR) | ❌ | ✅ (90+ languages) |
| Tables | ✅ (basic) | ✅ (high accuracy) |
| Equations / LaTeX | ❌ | ✅ |
| Code blocks | ❌ | ✅ |
| Forms | ❌ | ✅ |
| Headers/footers removal | ❌ | ✅ |
| Reading order detection | ❌ | ✅ |
| Images extraction | ✅ (embedded) | ✅ (with context) |
| Images → text (OCR) | ❌ | ✅ |
| EPUB | ✅ | ✅ |
| Markdown output | ✅ (via pymupdf4llm) | ✅ (native, higher quality) |
| Install size | ~25MB | ~3-5GB (PyTorch + models) |
| Speed | Instant | ~1-14s/page (CPU), ~0.2s/page (GPU) |
Decision: Use pymupdf unless you need OCR, equations, forms, or complex layout analysis.
If the user needs marker capabilities but the system lacks ~5GB free disk:
"This document needs OCR/advanced extraction (marker-pdf), which requires ~5GB for PyTorch and models. Your system has [X]GB free. Options: free up space, provide a URL so I can use web_extract, or I can try pymupdf which works for text-based PDFs but not scanned documents or equations."
pip install pymupdf pymupdf4llm
Via helper script:
python scripts/extract_pymupdf.py document.pdf # Plain text
python scripts/extract_pymupdf.py document.pdf --markdown # Markdown
python scripts/extract_pymupdf.py document.pdf --tables # Tables
python scripts/extract_pymupdf.py document.pdf --images out/ # Extract images
python scripts/extract_pymupdf.py document.pdf --metadata # Title, author, pages
python scripts/extract_pymupdf.py document.pdf --pages 0-4 # Specific pages
Inline:
python3 -c "
import pymupdf
doc = pymupdf.open('document.pdf')
for page in doc:
print(page.get_text())
"
# Check disk space first
python scripts/extract_marker.py --check
pip install marker-pdf
Via helper script:
python scripts/extract_marker.py document.pdf # Markdown
python scripts/extract_marker.py document.pdf --json # JSON with metadata
python scripts/extract_marker.py document.pdf --output_dir out/ # Save images
python scripts/extract_marker.py scanned.pdf # Scanned PDF (OCR)
python scripts/extract_marker.py document.pdf --use_llm # LLM-boosted accuracy
CLI (installed with marker-pdf):
marker_single document.pdf --output_dir ./output
marker /path/to/folder --workers 4 # Batch
# Abstract only (fast)
web_extract(urls=["https://arxiv.org/abs/2402.03300"])
# Full paper
web_extract(urls=["https://arxiv.org/pdf/2402.03300"])
# Search
web_search(query="arxiv GRPO reinforcement learning 2026")
web_extract is always first choice for URLs--help for full usage~/.cache/huggingface/ on first usepip install python-docx (better than OCR — parses actual structure)powerpoint skill (uses python-pptx)