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: {page.width} x {page.height}")
        print(f"  Blocks: {len(page.blocks)}")

        for block in page.blocks:
            if hasattr(block, 'text'):
                print(f"    Text block: {block.text[:50]}...")
            elif hasattr(block, 'image'):
                print(f"    Image block")

    cv.close()

analyze_pdf('document.pdf')

Best Practices

1. Check PDF Type: Native PDFs convert better than scanned
2. Preview First: Test with a few pages before full conversion
3. Handle Tables: Complex tables may need manual adjustment
4. Image Quality: Images are extracted at original resolution
5. Font Handling: Some fonts may substitute to system defaults

Common Patterns

Convert with Progress

from pdf2docx import Converter

def convert_with_progress(pdf_path, output_path):
    """Convert PDF with progress tracking."""
    cv = Converter(pdf_path)

    total_pages = len(cv.pages)
    print(f"Converting {total_pages} pages...")

    for i in range(total_pages):
        cv.convert(output_path, start=i, end=i+1)
        progress = (i + 1) / total_pages * 100
        print(f"Progress: {progress:.1f}%")

    cv.close()
    print("Conversion complete!")

Extract Tables Only

from pdf2docx import Converter
from docx import Document

def extract_tables_to_word(pdf_path, output_path):
    """Extract only tables from PDF to Word."""
    cv = Converter(pdf_path)

    # First do full conversion
    temp_path = 'temp_full.docx'
    cv.convert(temp_path)
    cv.close()

    # Open and extract tables
    doc = Document(temp_path)
    new_doc = Document()

    for table in doc.tables:
        # Copy table to new document
        new_table = new_doc.add_table(rows=0, cols=len(table.columns))

        for row in table.rows:
            new_row = new_table.add_row()
            for i, cell in enumerate(row.cells):
                new_row.cells[i].text = cell.text

        new_doc.add_paragraph()  # Add spacing

    new_doc.save(output_path)
    os.remove(temp_path)

Examples

Example 1: Contract Conversion

from pdf2docx import Converter
import os

def convert_contract(pdf_path):
    """Convert contract PDF to editable Word with metadata."""

    # Define output path
    base_name = os.path.splitext(pdf_path)[0]
    output_path = f"{base_name}_editable.docx"

    # Convert
    cv = Converter(pdf_path)

    # Check page count
    page_count = len(cv.pages)
    print(f"Processing {page_count} pages...")

    # Convert all pages
    cv.convert(output_path)
    cv.close()

    print(f"Created: {output_path}")
    print(f"File size: {os.path.getsize(output_path) / 1024:.1f} KB")

    return output_path

# Usage
result = convert_contract('contract.pdf')

Example 2: Selective Page Conversion

from pdf2docx import Converter

def convert_selected_pages(pdf_path, page_ranges, output_path):
    """Convert specific page ranges to Word.

    page_ranges: List of tuples like [(1, 3), (5, 7)] for pages 1-3 and 5-7
    """
    cv = Converter(pdf_path)

    # Convert pages (0-indexed internally)
    all_pages = []
    for start, end in page_ranges:
        all_pages.extend(range(start - 1, end))  # Convert to 0-indexed

    cv.convert(output_path, pages=all_pages)
    cv.close()

    print(f"Converted pages: {page_ranges}")
    return output_path

# Convert pages 1-5 and 10-15
convert_selected_pages(
    'long_document.pdf',
    [(1, 5), (10, 15)],
    'selected_pages.docx'
)

Example 3: PDF Report to Editable Template

from pdf2docx import Converter
from docx import Document

def pdf_to_template(pdf_path, output_path):
    """Convert PDF report to Word template with placeholders."""

    # Convert PDF to Word
    cv = Converter(pdf_path)
    cv.convert(output_path)
    cv.close()

    # Open and add placeholder fields
    doc = Document(output_path)

    # Replace common fields with placeholders
    replacements = {
        'Company Name': '[COMPANY_NAME]',
        'Date:': 'Date: [DATE]',
        'Prepared by:': 'Prepared by: [AUTHOR]',
    }

    for para in doc.paragraphs:
        for old, new in replacements.items():
            if old in para.text:
                para.text = para.text.replace(old, new)

    # Also check tables
    for table in doc.tables:
        for row in table.rows:
            for cell in row.cells:
                for old, new in replacements.items():
                    if old in cell.text:
                        cell.text = cell.text.replace(old, new)

    doc.save(output_path)
    print(f"Template created: {output_path}")

pdf_to_template('annual_report.pdf', 'report_template.docx')

Example 4: Bulk Invoice Processing

from pdf2docx import Converter
from pathlib import Path
import json

def process_invoices(input_folder, output_folder):
    """Convert PDF invoices to editable Word documents."""

    input_path = Path(input_folder)
    output_path = Path(output_folder)
    output_path.mkdir(exist_ok=True)

    results = []

    for pdf_file in input_path.glob('*.pdf'):
        output_file = output_path / pdf_file.with_suffix('.docx').name

        try:
            cv = Converter(str(pdf_file))
            cv.convert(str(output_file))
            cv.close()

            results.append({
                'file': pdf_file.name,
                'status': 'success',
                'output': str(output_file)
            })

        except Exception as e:
            results.append({
                'file': pdf_file.name,
                'status': 'error',
                'error': str(e)
            })

    # Save results log
    with open(output_path / 'conversion_log.json', 'w') as f:
        json.dump(results, f, indent=2)

    # Summary
    success = sum(1 for r in results if r['status'] == 'success')
    print(f"Converted {success}/{len(results)} files")

    return results

results = process_invoices('./invoices_pdf', './invoices_word')

Limitations

• Scanned PDFs require OCR preprocessing
• Complex layouts may not convert perfectly
• Some fonts may not be available
• Watermarks are included in conversion
• Protected/encrypted PDFs need password

Installation

pip install pdf2docx

# For image handling
pip install Pillow

Resources

• GitHub Repository
• Documentation
• PyPI Package

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