PDF to Word Skill

Overview

This skill enables conversion from PDF to editable Word documents using pdf2docx - a Python library that preserves layout, tables, images, and text formatting. Unlike OCR-based solutions, pdf2docx extracts native PDF content for accurate conversion.

How to Use

1. Provide the PDF file you want to convert
2. Optionally specify pages or conversion options
3. I'll convert it to an editable Word document

Example prompts:

• "Convert this PDF report to an editable Word document"
• "Turn pages 1-5 of this PDF into Word format"
• "Extract this scanned document as editable text"
• "Convert this PDF contract to Word for editing"

Domain Knowledge

pdf2docx Fundamentals

from pdf2docx import Converter

# Basic conversion
cv = Converter('input.pdf')
cv.convert('output.docx')
cv.close()

# Or using context manager
with Converter('input.pdf') as cv:
    cv.convert('output.docx')

Conversion Options

from pdf2docx import Converter

cv = Converter('input.pdf')

# Full document
cv.convert('output.docx')

# Specific pages (0-indexed)
cv.convert('output.docx', start=0, end=5)

# Single page
cv.convert('output.docx', pages=[0])

# Multiple specific pages
cv.convert('output.docx', pages=[0, 2, 4])

cv.close()

Advanced Options

from pdf2docx import Converter

cv = Converter('input.pdf')

cv.convert(
    'output.docx',
    start=0,                    # Start page (0-indexed)
    end=None,                   # End page (None = last page)
    pages=None,                 # Specific pages list
    password=None,              # PDF password if encrypted
    min_section_height=20.0,    # Minimum height for section
    connected_border_tolerance=0.5,  # Border detection tolerance
    line_overlap_threshold=0.9, # Line merging threshold
    line_break_width_ratio=0.5, # Line break detection
    line_break_free_space_ratio=0.1,
    line_separate_threshold=5,  # Vertical line separation
    new_paragraph_free_space_ratio=0.85,
    float_image_ignorable_gap=5,
    page_margin_factor_top=0.5,
    page_margin_factor_bottom=0.5,
)

cv.close()

Handling Different PDF Types

Native PDFs (Text-based)

# Works best with native PDFs
cv = Converter('native_pdf.pdf')
cv.convert('output.docx')
cv.close()

Scanned PDFs (Image-based)

# For scanned PDFs, use OCR first
# pdf2docx works best with native text PDFs
# Consider using pytesseract or PaddleOCR first

import pytesseract
from pdf2image import convert_from_path

# Convert PDF pages to images
images = convert_from_path('scanned.pdf')

# OCR each page
text = ''
for img in images:
    text += pytesseract.image_to_string(img)

# Then create Word document from text

Python Integration

from pdf2docx import Converter
import os

def pdf_to_word(pdf_path, output_path=None, pages=None):
    """Convert PDF to Word document."""
    if output_path is None:
        output_path = pdf_path.replace('.pdf', '.docx')
    
    cv = Converter(pdf_path)
    
    if pages:
        cv.convert(output_path, pages=pages)
    else:
        cv.convert(output_path)
    
    cv.close()
    
    return output_path

# Usage
result = pdf_to_word('document.pdf')
print(f"Created: {result}")

Batch Conversion

from pdf2docx import Converter
from pathlib import Path
from concurrent.futures import ThreadPoolExecutor

def convert_single(pdf_path, output_dir):
    """Convert single PDF to Word."""
    output_path = output_dir / pdf_path.with_suffix('.docx').name
    
    try:
        cv = Converter(str(pdf_path))
        cv.convert(str(output_path))
        cv.close()
        return f"Success: {pdf_path.name}"
    except Exception as e:
        return f"Error: {pdf_path.name} - {e}"

def batch_convert(input_dir, output_dir, max_workers=4):
    """Convert all PDFs in directory."""
    input_path = Path(input_dir)
    output_path = Path(output_dir)
    output_path.mkdir(exist_ok=True)
    
    pdf_files = list(input_path.glob('*.pdf'))
    
    with ThreadPoolExecutor(max_workers=max_workers) as executor:
        futures = [
            executor.submit(convert_single, pdf, output_path)
            for pdf in pdf_files
        ]
        
        for future in futures:
            print(future.result())

batch_convert('./pdfs', './word_docs')

Parsing PDF Structure

from pdf2docx import Converter

def analyze_pdf(pdf_path):
    """Analyze PDF structure before conversion."""
    cv = Converter(pdf_path)
    
    for i, page in enumerate(cv.pages):
        print(f"Page {i+1}:")
        print(f"  Size: