Extract PDF Text

Extract text from PDF files using PyMuPDF. Parse tables, forms, and complex layouts. Supports OCR for scanned documents.

When to Use

Agent needs to extract text from PDFs. Use PyMuPDF (fitz) for fast local extraction. Works with text-based documents, scanned pages with OCR, forms, and complex layouts.

Quick Reference

Topic	File
Code examples	examples.md
OCR setup	ocr.md
Troubleshooting	troubleshooting.md

Core Rules

1. Install PyMuPDF First

bash

pip install PyMuPDF

Import as fitz (historical name):

python

import fitz  # PyMuPDF

2. Basic Text Extraction

python

import fitz

doc = fitz.open("document.pdf")
text = ""
for page in doc:
    text += page.get_text()
doc.close()

3. Pick the Right Method

PDF Type	Method
Text-based	page.get_text() — fast, accurate
Scanned	OCR with pytesseract — slower
Mixed	Check each page, use OCR when needed

4. Check for Text Before OCR

python

def needs_ocr(page):
    text = page.get_text().strip()
    return len(text) < 50  # Likely scanned if very little text

5. Handle Errors Gracefully

python

try:
    doc = fitz.open(path)
except fitz.FileDataError:
    print("Invalid or corrupted PDF")
except fitz.PasswordError:
    doc = fitz.open(path, password="secret")

Extraction Traps

Trap	What Happens	Fix
OCR on text PDF	Slow + worse accuracy	Check get_text() first
Forget to close doc	Memory leak	Use with or doc.close()
Assume page order	Wrong reading flow	Use sort=True in get_text()
Ignore encoding	Garbled characters	PyMuPDF handles UTF-8

Scope

This skill provides instructions for using PyMuPDF to extract PDF text.

This skill ONLY:

• Gives code examples for PyMuPDF
• Explains OCR setup when needed
• Troubleshoots common issues

This skill NEVER:

• Accesses files without user request
• Sends data externally
• Modifies original PDFs

Security & Privacy

All processing is local:

• PyMuPDF runs entirely on your machine
• No external API calls
• No data leaves your system

Output Formats

Plain Text

python

text = page.get_text()

Structured (dict)

python

blocks = page.get_text("dict")["blocks"]
for b in blocks:
    if b["type"] == 0:  # text block
        for line in b["lines"]:
            for span in line["spans"]:
                print(span["text"], span["size"])

JSON

python

import json
data = page.get_text("json")
parsed = json.loads(data)

Full Example

python

import fitz

def extract_pdf(path):
    """Extract text from PDF, with OCR fallback for scanned pages."""
    doc = fitz.open(path)
    results = []
    
    for i, page in enumerate(doc):
        text = page.get_text()
        method = "text"
        
        # If very little text, might be scanned
        if len(text.strip()) < 50:
            # OCR would go here (see ocr.md)
            method = "needs_ocr"
        
        results.append({
            "page": i + 1,
            "text": text,
            "method": method
        })
    
    doc.close()
    return {
        "pages": len(results),
        "content": results,
        "word_count": sum(len(r["text"].split()) for r in results)
    }

# Usage
result = extract_pdf("document.pdf")
print(f"Extracted {result['word_count']} words from {result['pages']} pages")

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