PDF Text & Table Extractor

What This Does

This workflow enables precise extraction of text, tables, and metadata from PDF documents using pdfplumber - the go-to library for PDF data extraction. Unlike basic PDF readers, pdfplumber provides detailed character-level positioning, accurate table detection, and visual debugging.

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Quick Start

Step 1: Create a Project Folder

mkdir -p ~/Documents/PdfExtraction

Step 2: Download the Template

Click Download above, then:

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

Step 3: Start Working

cd ~/Documents/PdfExtraction
claude

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How to Use

1. Provide the PDF file you want to extract from
2. Specify what you need: text, tables, images, or metadata
3. I'll generate pdfplumber code and execute it

Example prompts:

• "Extract all tables from this financial report"
• "Get text from pages 5-10 of this document"
• "Find and extract the invoice total from this PDF"
• "Convert this PDF table to CSV/Excel"

Best Practices

1. Debug Visually: Use to_image() to understand PDF structure
2. Tune Table Settings: Adjust tolerances for your specific PDF
3. Handle Scanned PDFs: Use OCR first (this workflow is for native text)
4. Process Page by Page: For large PDFs, avoid loading all at once
5. Check for Text: Some PDFs are images - verify text exists

Examples

Example 1: Financial Report Table Extraction

import pdfplumber
import pandas as pd

def extract_financial_tables(pdf_path):
    """Extract tables from financial report and save to Excel."""
    
    with pdfplumber.open(pdf_path) as pdf:
        all_tables = []
        
        for page_num, page in enumerate(pdf.pages):
            # Debug: save table visualization
            im = page.to_image()
            im.debug_tablefinder()
            im.save(f'debug_page_{page_num+1}.png')
            
            # Extract tables
            tables = page.extract_tables({
                "vertical_strategy": "lines",
                "horizontal_strategy": "lines",
                "snap_tolerance": 5,
            })
            
            for table in tables:
                if table and len(table) > 1:
                    # Clean data
                    clean_table = []
                    for row in table:
                        clean_row = [cell.strip() if cell else '' for cell in row]
                        clean_table.append(clean_row)
                    
                    df = pd.DataFrame(clean_table[1:], columns=clean_table[0])
                    df['Source Page'] = page_num + 1
                    all_tables.append(df)
        
        # Save to Excel with multiple sheets
        with pd.ExcelWriter('extracted_tables.xlsx') as writer:
            for i, df in enumerate(all_tables):
                df.to_excel(writer, sheet_name=f'Table_{i+1}', index=False)
        
        return all_tables

tables = extract_financial_tables('annual_report.pdf')
print(f"Extracted {len(tables)} tables")

Example 2: Invoice Data Extraction

import pdfplumber
import re
from datetime import datetime

def extract_invoice_data(pdf_path):
    """Extract structured data from invoice PDF."""
    
    data = {
        'invoice_number': None,
        'date': None,
        'total': None,
        'line_items': []
    }
    
    with pdfplumber.open(pdf_path) as pdf:
        page = pdf.pages[0]
        text = page.extract_text()
        
        # Extract invoice number
        inv_match = re.search(r'Invoice\s*#?\s*:?\s*(\w+)', text, re.IGNORECASE)
        if inv_match:
            data['invoice_number'] = inv_match.group(1)
        
        # Extract date
        date_match = re.search(r'Date\s*:?\s*(\d{1,2}[/-]\d{1,2}[/-]\d{2,4})', text)
        if date_match:
            data['date'] = date_match.group(1)
        
        # Extract total
        total_match = re.search(r'Total\s*:?\s*\$?([\d,]+\.?\d*)', text, re.IGNORECASE)
        if total_match:
            data['total'] = float(total_match.group(1).replace(',', ''))
        
        # Extract line items from table
        tables = page.extract_tables()
        for table in tables:
            if table and any('description' in str(row).lower() for row in table[:2]):
                # Found line items table
                for row in table[1:]:  # Skip header
                    if row and len(row) >= 3:
                        data['line_items'].append({
                            'description': row[0],
                            'quantity': row[1] if len(row) > 1 else None,
                            'amount': row[-1]
                        })
    
    return data

invoice = extract_invoice_data('invoice.pdf')
print(f"Invoice #{invoice['invoice_number']}")
print(f"Total: ${invoice['total']}")

Example 3: Resume/CV Parser

Limitations

• Cannot extract from scanned/image PDFs (use OCR first)
• Complex layouts may need manual tuning
• Some PDF encryption types not supported
• Embedded fonts may affect text extraction
• No direct PDF editing capability

Installation

pip install pdfplumber

# For image debugging (optional)
pip install Pillow