Extract text from PDFs/scans (pymupdf, marker-pdf).
Skill metadata
| Source | Bundled (installed by default) |
| Path | skills/productivity/ocr-and-documents |
| Version | 2.3.0 |
| Author | Hermes Agent |
| License | MIT |
| Platforms | linux, macos, windows |
| Tags | PDF, Documents, Research, Arxiv, Text-Extraction, OCR |
| Related skills | pdf, docx, powerpoint |
Reference: full SKILL.md
The following is the complete skill definition that Hermes loads when this skill is triggered. This is what the agent sees as instructions when the skill is active.
PDF & Document Extraction
For DOCX: see the docx skill (create/edit) or use python-docx for structured reads.
For PPTX: see the powerpoint skill (full create/read/edit support).
For PDF manipulation (merge, split, forms, watermarks, creation): see the pdf skill.
This skill covers text extraction from PDFs and scanned documents.
Step 1: Remote URL Available?
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.
Step 2: Choose Local Extractor
| 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."
pymupdf (lightweight)
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())
"
marker-pdf (high-quality OCR)
# 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
Arxiv Papers
# 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")
Split, Merge & Search
pymupdf handles these natively — use execute_code or inline Python:
# Split: extract pages 1-5 to a new PDF
import pymupdf
doc = pymupdf.open("report.pdf")
new = pymupdf.open()
for i in range(5):
new.insert_pdf(doc, from_page=i, to_page=i)
new.save("pages_1-5.pdf")
# Merge multiple PDFs
import pymupdf
result = pymupdf.open()
for path in ["a.pdf", "b.pdf", "c.pdf"]:
result.insert_pdf(pymupdf.open(path))
result.save("merged.pdf")
# Search for text across all pages
import pymupdf
doc = pymupdf.open("report.pdf")
for i, page in enumerate(doc):
results = page.search_for("revenue")
if results:
print(f"Page {i+1}: {len(results)} match(es)")
print(page.get_text("text"))
No extra dependencies needed — pymupdf covers split, merge, search, and text extraction in one package.
Notes
web_extractis always first choice for URLs- pymupdf is the safe default — instant, no models, works everywhere
- marker-pdf is for OCR, scanned docs, equations, complex layouts — install only when needed
- Both helper scripts accept
--helpfor full usage - marker-pdf downloads ~2.5GB of models to
~/.cache/huggingface/on first use - For Word docs:
pip install python-docx(better than OCR — parses actual structure) - For PowerPoint: see the
powerpointskill (uses python-pptx)