Ocr And Documents

---
name: argentos-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: ArgentOS
license: MIT
metadata:
  argent:
    tags:
      - PDF
      - Documents
      - Research
      - Arxiv
      - Text-Extraction
      - OCR
    related_skills:
      - powerpoint
  imported_from:
    original_name: ocr-and-documents
    source: upstream skill profile
---

# 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

| 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)

```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)