name: ocr-and-documents

description: Extract text from PDFs and scanned documents. Use web_extract for remote URLs, pymupdf for local text-based PDFs, marker-pdf for OCR/scanned docs. For DOCX use python-docx, for PPTX see the powerpoint skill.

version: 2.3.0

author: Hermes Agent

license: MIT

metadata:

hermes:

tags: [PDF, Documents, Research, Arxiv, Text-Extraction, OCR]

related_skills: [powerpoint]

PDF & Document Extraction

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.

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

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)

`bash

pip install pymupdf pymupdf4llm

`

Via helper script:

`bash

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:

`bash

python3 -c "

import pymupdf

doc = pymupdf.open('document.pdf')

for page in doc:

print(page.get_text())

"

`

marker-pdf (high-quality OCR)

`bash

Check disk space first

python scripts/extract_marker.py --check

pip install marker-pdf

`

Via helper script:

`bash

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):

`bash

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:

`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")

`

`python

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")

`

`python

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_extract is 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 --help for 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 powerpoint skill (uses python-pptx)