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name: ocr-and-documents description: "Extract text from PDFs/scans (pymupdf, marker-pdf)." version: 2.3.0 author: Hermes Agent license: MIT platforms: [linux, macos, windows] metadata: hermes: tags: [PDF, Documents, Research, Arxiv, Text-Extraction, OCR] related_skills: [pdf, docx, powerpoint]

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")

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