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| name | doc-parser |
| description | > |
| version | 1.0 |
| author | claude-office-skills |
| license | MIT |
| category | parsing |
| tags | ["parsing","extraction","layout","docling"] |
| department | All |
| models | {"recommended":["claude-sonnet-4","claude-opus-4"],"compatible":["claude-3-5-sonnet","gpt-4","gpt-4o"]} |
| mcp | {"server":"office-mcp","tools":["analyze_document_structure","extract_text_from_pdf"]} |
| capabilities | ["document_parsing","layout_analysis"] |
| languages | ["en","zh"] |
This skill enables advanced document parsing using docling - IBM's state-of-the-art document understanding library. Parse complex PDFs, Word documents, and images while preserving structure, extracting tables, figures, and handling multi-column layouts.
Example prompts:
from docling.document_converter import DocumentConverter
# Initialize converter
converter = DocumentConverter()
# Convert document
result = converter.convert("document.pdf")
# Access parsed content
doc = result.document
print(doc.export_to_markdown())
| Format | Extension | Notes |
|---|---|---|
| Native and scanned | ||
| Word | .docx | Full structure preserved |
| PowerPoint | .pptx | Slides as sections |
| Images | .png, .jpg | OCR + layout analysis |
| HTML | .html | Structure preserved |
from docling.document_converter import DocumentConverter
# Create converter
converter = DocumentConverter()
# Convert single document
result = converter.convert("report.pdf")
# Access document
doc = result.document
# Export options
markdown = doc.export_to_markdown()
text = doc.export_to_text()
json_doc = doc.export_to_dict()
from docling.document_converter import DocumentConverter
from docling.datamodel.base_models import InputFormat
from docling.datamodel.pipeline_options import PdfPipelineOptions
# Configure pipeline
pipeline_options = PdfPipelineOptions()
pipeline_options.do_ocr = True
pipeline_options.do_table_structure = True
pipeline_options.table_structure_options.do_cell_matching = True
# Create converter with options
converter = DocumentConverter(
allowed_formats=[InputFormat.PDF, InputFormat.DOCX],
pdf_backend_options=pipeline_options
)
result = converter.convert("document.pdf")
# Document hierarchy
doc = result.document
# Access metadata
print(doc.name)
print(doc.origin)
# Iterate through content
for element in doc.iterate_items():
print(f"Type: {element.type}")
print(f"Text: {element.text}")
if element.type == "table":
print(f"Rows: {len(element.data.table_cells)}")
from docling.document_converter import DocumentConverter
import pandas as pd
def extract_tables(doc_path):
"""Extract all tables from document."""
converter = DocumentConverter()
result = converter.convert(doc_path)
doc = result.document
tables = []
for element in doc.iterate_items():
if element.type == "table":
# Get table data
table_data = element.export_to_dataframe()
tables.append({
'page': element.prov[0].page_no if element.prov else None,
'dataframe': table_data
})
return tables
# Usage
tables = extract_tables("report.pdf")
for i, table in enumerate(tables):
print(f"Table {i+1} on page {table['page']}:")
print(table['dataframe'])
def extract_figures(doc_path, output_dir):
"""Extract figures with captions."""
import os
converter = DocumentConverter()
result = converter.convert(doc_path)
doc = result.document
figures = []
os.makedirs(output_dir, exist_ok=True)
for element in doc.iterate_items():
if element.type == "picture":
figure_info = {
'caption': element.caption if hasattr(element, 'caption') else None,
'page': element.prov[0].page_no if element.prov else None,
}
# Save image if available
if hasattr(element, 'image'):
img_path = os.path.join(output_dir, f"figure_{len(figures)+1}.png")
element.image.save(img_path)
figure_info['path'] = img_path
figures.append(figure_info)
return figures
from docling.document_converter import DocumentConverter
def parse_multicolumn(doc_path):
"""Parse document with multi-column layout."""
converter = DocumentConverter()
result = converter.convert(doc_path)
doc = result.document
# docling automatically handles column detection
# Text is returned in reading order
structured_content = []
for element in doc.iterate_items():
content_item = {
'type': element.type,
'text': element.text if hasattr(element, 'text') else None,
'level': element.level if hasattr(element, 'level') else None,
}
# Add bounding box if available
if element.prov:
content_item['bbox'] = element.prov[0].bbox
content_item['page'] = element.prov[0].page_no
structured_content.append(content_item)
return structured_content
from docling.document_converter import DocumentConverter
converter = DocumentConverter()
result = converter.convert("document.pdf")
doc = result.document
# Markdown export
markdown = doc.export_to_markdown()
with open("output.md", "w") as f:
f.write(markdown)
# Plain text
text = doc.export_to_text()
# JSON/dict format
json_doc = doc.export_to_dict()
# HTML format (if supported)
# html = doc.export_to_html()
from docling.document_converter import DocumentConverter
from pathlib import Path
from concurrent.futures import ThreadPoolExecutor
def batch_parse(input_dir, output_dir, max_workers=4):
"""Parse multiple documents in parallel."""
input_path = Path(input_dir)
output_path = Path(output_dir)
output_path.mkdir(exist_ok=True)
converter = DocumentConverter()
def process_single(doc_path):
try:
result = converter.convert(str(doc_path))
md = result.document.export_to_markdown()
out_file = output_path / f"{doc_path.stem}.md"
with open(out_file, 'w') as f:
f.write(md)
return {'file': str(doc_path), 'status': 'success'}
except Exception as e:
return {'file': str(doc_path), 'status': 'error', 'error': str(e)}
docs = list(input_path.glob('*.pdf')) + list(input_path.glob('*.docx'))
with ThreadPoolExecutor(max_workers=max_workers) as executor:
results = list(executor.map(process_single, docs))
return results
def parse_academic_paper(pdf_path):
"""Parse academic paper structure."""
converter = DocumentConverter()
result = converter.convert(pdf_path)
doc = result.document
paper = {
'title': None,
'abstract': None,
'sections': [],
'references': [],
'tables': [],
'figures': []
}
current_section = None
for element in doc.iterate_items():
text = element.text if hasattr(element, 'text') else ''
if element.type == 'title':
paper['title'] = text
elif element.type == 'heading':
if 'abstract' in text.lower():
current_section = 'abstract'
elif 'reference' in text.lower():
current_section = 'references'
else:
paper['sections'].append({
'title': text,
'content': ''
})
current_section = 'section'
elif element.type == 'paragraph':
if current_section == 'abstract':
paper['abstract'] = text
elif current_section == 'section' and paper['sections']:
paper['sections'][-1]['content'] += text + '\n'
elif element.type == 'table':
paper['tables'].append({
'caption': element.caption if hasattr(element, 'caption') else None,
'data': element.export_to_dataframe() if hasattr(element, 'export_to_dataframe') else None
})
return paper
def parse_business_report(doc_path):
"""Parse business report into structured format."""
converter = DocumentConverter()
result = converter.convert(doc_path)
doc = result.document
report = {
'metadata': {
'title': None,
'date': None,
'author': None
},
'executive_summary': None,
'sections': [],
'key_metrics': [],
'recommendations': []
}
# Parse document structure
for element in doc.iterate_items():
# Implement parsing logic based on document structure
pass
return report
from docling.document_converter import DocumentConverter
def parse_financial_report(pdf_path):
"""Extract structured data from financial report."""
converter = DocumentConverter()
result = converter.convert(pdf_path)
doc = result.document
financial_data = {
'income_statement': None,
'balance_sheet': None,
'cash_flow': None,
'notes': []
}
# Extract tables
tables = []
for element in doc.iterate_items():
if element.type == 'table':
table_df = element.export_to_dataframe()
# Identify table type
if 'revenue' in str(table_df).lower() or 'income' in str(table_df).lower():
financial_data['income_statement'] = table_df
elif 'asset' in str(table_df).lower() or 'liabilities' in str(table_df).lower():
financial_data['balance_sheet'] = table_df
elif 'cash' in str(table_df).lower():
financial_data['cash_flow'] = table_df
else:
tables.append(table_df)
# Extract markdown for notes
financial_data['markdown'] = doc.export_to_markdown()
return financial_data
report = parse_financial_report('annual_report.pdf')
print("Income Statement:")
print(report['income_statement'])
from docling.document_converter import DocumentConverter
def parse_technical_docs(doc_path):
"""Parse technical documentation."""
converter = DocumentConverter()
result = converter.convert(doc_path)
doc = result.document
documentation = {
'title': None,
'version': None,
'sections': [],
'code_blocks': [],
'diagrams': []
}
current_section = None
for element in doc.iterate_items():
if element.type == 'title':
documentation['title'] = element.text
elif element.type == 'heading':
current_section = {
'title': element.text,
'level': element.level if hasattr(element, 'level') else 1,
'content': []
}
documentation['sections'].append(current_section)
elif element.type == 'code':
if current_section:
current_section['content'].append({
'type': 'code',
'content': element.text
})
documentation['code_blocks'].append(element.text)
elif element.type == 'picture':
documentation['diagrams'].append({
'page': element.prov[0].page_no if element.prov else None,
'caption': element.caption if hasattr(element, 'caption') else None
})
return documentation
docs = parse_technical_docs('api_documentation.pdf')
print(f"Title: {docs['title']}")
print(f"Sections: {len(docs['sections'])}")
from docling.document_converter import DocumentConverter
def analyze_contract(pdf_path):
"""Parse contract document for key clauses."""
converter = DocumentConverter()
result = converter.convert(pdf_path)
doc = result.document
contract = {
'parties': [],
'clauses': [],
'dates': [],
'amounts': [],
'full_text': doc.export_to_text()
}
import re
# Extract dates
date_pattern = r'\b\d{1,2}[/-]\d{1,2}[/-]\d{2,4}\b|\b(?:Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)[a-z]* \d{1,2},? \d{4}\b'
contract['dates'] = re.findall(date_pattern, contract['full_text'], re.IGNORECASE)
# Extract monetary amounts
amount_pattern = r'\$[\d,]+(?:\.\d{2})?|\b\d+(?:,\d{3})*(?:\.\d{2})?\s*(?:USD|dollars)\b'
contract['amounts'] = re.findall(amount_pattern, contract['full_text'], re.IGNORECASE)
# Parse sections as clauses
for element in doc.iterate_items():
if element.type == 'heading':
contract['clauses'].append({
'title': element.text,
'content': ''
})
elif element.type == 'paragraph' and contract['clauses']:
contract['clauses'][-1]['content'] += element.text + '\n'
return contract
contract_data = analyze_contract('agreement.pdf')
print(f"Key dates: {contract_data['dates']}")
print(f"Amounts: {contract_data['amounts']}")
pip install docling
# For full functionality
pip install docling[all]
# For OCR support
pip install docling[ocr]
Based on SOC occupation classification
