Document Parser

What This Does

This workflow 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.

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Quick Start

Step 1: Create a Project Folder

mkdir -p ~/Documents/DocParser

Step 2: Download the Template

Click Download above, then:

mv ~/Downloads/CLAUDE.md ~/Documents/DocParser/

Step 3: Start Working

cd ~/Documents/DocParser
claude

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How to Use

1. Provide the document to parse
2. Specify what you want to extract (text, tables, figures, etc.)
3. I'll parse it and return structured data

Example prompts:

• "Parse this PDF and extract all tables"
• "Convert this academic paper to structured markdown"
• "Extract figures and captions from this document"
• "Parse this report preserving the document structure"

Best Practices

1. Use Appropriate Pipeline: Configure for your document type
2. Handle Large Documents: Process in chunks if needed
3. Verify Table Extraction: Complex tables may need review
4. Check OCR Quality: Enable OCR for scanned documents
5. Cache Results: Store parsed documents for reuse

Examples

Example 1: Parse Financial 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'])

Example 2: Technical Documentation Parser

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'])}")

Example 3: Contract Analysis

Limitations

• Very large documents may require chunking
• Handwritten content needs OCR preprocessing
• Complex nested tables may need manual review
• Some PDF types (encrypted) not supported
• GPU recommended for best performance

Installation

pip install docling

# For full functionality
pip install docling[all]

# For OCR support
pip install docling[ocr]