pdf-to-docx

name pdf-to-docx description Convert PDF files to editable Word documents using pdf2docx version 1.0 author claude-office-skills license MIT category pdf tags ["pdf","docx","conversion"] department All models {"recommended":["claude-sonnet-4","claude-opus-4"],"compatible":["claude-3-5-sonnet","gpt-4","gpt-4o"]} mcp {"server":"office-mcp","tools":["pdf_to_docx"]} capabilities ["format_conversion","editable_documents"] languages ["en","zh"] 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 Provide the PDF file you want to convert Optionally specify pages or conversion options 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 Check PDF Type : Native PDFs convert better than scanned Preview First : Test with a few pages before full conversion Handle Tables : Complex tables may need manual adjustment Image Quality : Images are extracted at original resolution 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: .1 f} %" ) 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 : .1 f} 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