Table Extraction from Documents

What This Does

This workflow enables precise extraction of tables from PDF documents using camelot - the gold standard for PDF table extraction. Handle complex tables with merged cells, borderless tables, and multi-page layouts with high accuracy.

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Quick Start

Step 1: Create a Project Folder

mkdir -p ~/Documents/TableExtractor

Step 2: Download the Template

Click Download above, then:

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

Step 3: Start Working

cd ~/Documents/TableExtractor
claude

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How to Use

1. Provide the PDF containing tables
2. Optionally specify pages or table detection method
3. I'll extract tables as pandas DataFrames

Example prompts:

• "Extract all tables from this PDF"
• "Get the table on page 5 of this report"
• "Extract borderless tables from this document"
• "Convert PDF tables to Excel format"

Best Practices

1. Try Both Methods: Lattice for bordered, stream for borderless
2. Check Accuracy Score: Above 90% is usually good
3. Use Visual Debugging: Understand extraction results
4. Specify Areas: For PDFs with multiple table types
5. Handle Headers: First row often needs special treatment

Examples

Example 1: Financial Statement Extraction

import camelot
import pandas as pd

def extract_financial_tables(pdf_path):
    """Extract financial tables from annual report."""
    
    # Extract all tables
    tables = camelot.read_pdf(pdf_path, pages='all', flavor='lattice')
    
    financial_data = {
        'income_statement': None,
        'balance_sheet': None,
        'cash_flow': None,
        'other_tables': []
    }
    
    for table in tables:
        df = table.df
        text = df.to_string().lower()
        
        # Identify table type
        if 'revenue' in text or 'sales' in text:
            if 'operating income' in text or 'net income' in text:
                financial_data['income_statement'] = df
        elif 'asset' in text and 'liabilities' in text:
            financial_data['balance_sheet'] = df
        elif 'cash flow' in text or 'operating activities' in text:
            financial_data['cash_flow'] = df
        else:
            financial_data['other_tables'].append({
                'page': table.page,
                'data': df,
                'accuracy': table.accuracy
            })
    
    return financial_data

financials = extract_financial_tables('annual_report.pdf')
if financials['income_statement'] is not None:
    print("Income Statement found:")
    print(financials['income_statement'])

Example 2: Scientific Data Extraction

import camelot
import pandas as pd

def extract_research_data(pdf_path, pages='all'):
    """Extract data tables from research paper."""
    
    # Try lattice for bordered tables
    tables = camelot.read_pdf(pdf_path, pages=pages, flavor='lattice')
    
    if not tables or all(t.accuracy < 70 for t in tables):
        # Fall back to stream for borderless
        tables = camelot.read_pdf(pdf_path, pages=pages, flavor='stream')
    
    extracted_data = []
    
    for table in tables:
        df = table.df
        
        # Clean up the DataFrame
        # Set first row as header if it looks like one
        if not df.iloc[0].str.contains(r'\d').any():
            df.columns = df.iloc[0]
            df = df[1:]
            df = df.reset_index(drop=True)
        
        extracted_data.append({
            'page': table.page,
            'accuracy': table.accuracy,
            'data': df
        })
    
    return extracted_data

data = extract_research_data('research_paper.pdf')
for i, item in enumerate(data):
    print(f"Table {i+1} (Page {item['page']}, Accuracy: {item['accuracy']}%):")
    print(item['data'].head())

Example 3: Invoice Line Items

Limitations

• Encrypted PDFs not supported
• Image-based PDFs need OCR preprocessing
• Very complex merged cells may need tuning
• Rotated tables require preprocessing
• Large PDFs may need page-by-page processing

Installation

pip install camelot-py[cv]

# Additional dependencies
# macOS
brew install ghostscript tcl-tk

# Ubuntu
apt-get install ghostscript python3-tk