pdf-to-docx

仓库: claude-office-skills/skills
安装量: 253
排名: #3455

安装

npx skills add https://github.com/claude-office-skills/skills --skill pdf-to-docx

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 : .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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