Python PDF Processing: Automate Document Workflows

December 30, 2025

Automate PDF Processing with Python: Extract, Transform, and Generate Documents

PDFs are everywhere in business—invoices, contracts, reports, forms. Manually processing them wastes hours. Python makes PDF automation accessible, from simple text extraction to complex document generation. Here's how to automate your PDF workflows.

Common PDF Automation Use Cases

Extract text and data from invoices
Merge multiple PDFs into one
Split large documents into sections
Fill PDF forms programmatically
Convert PDFs to other formats
Add watermarks or stamps
Generate reports as PDFs
OCR scanned documents

Essential Python PDF Libraries

PyPDF2 / PyPDF

Basic PDF manipulation:

Merge and split PDFs
Extract text (limited formatting)
Rotate pages
Add watermarks
Encrypt/decrypt PDFs

pdfplumber

Better text and table extraction:

Extract text with position info
Parse tables accurately
Handle complex layouts
Good for invoices and statements

ReportLab

Generate PDFs from scratch:

Create professional documents
Add images, tables, charts
Full layout control
Template-based generation

PyMuPDF (fitz)

High-performance PDF handling:

Fast processing
Image extraction
Text search and highlight
Annotation handling

pytesseract

OCR for scanned documents:

Extract text from images
Works with scanned PDFs
Multiple language support
Combine with pdf2image

Use Case 1: Invoice Data Extraction

Extract key data from invoice PDFs:

import pdfplumber

def extract_invoice_data(pdf_path):
    with pdfplumber.open(pdf_path) as pdf:
        page = pdf.pages[0]
        text = page.extract_text()

        # Parse for common invoice fields
        data = {
            'invoice_number': find_pattern(text, r'Invoice[:#]?\s*(\w+)'),
            'date': find_pattern(text, r'Date[:]?\s*([\d/\-]+)'),
            'total': find_pattern(text, r'Total[:]?\s*\$?([\d,.]+)'),
            'vendor': extract_vendor_name(text)
        }

        # Extract line items from tables
        tables = page.extract_tables()
        data['line_items'] = parse_line_items(tables)

        return data

Use Case 2: Report Generation

Generate professional PDF reports:

from reportlab.lib.pagesizes import letter
from reportlab.platypus import SimpleDocTemplate, Table, Paragraph
from reportlab.lib.styles import getSampleStyleSheet

def generate_report(data, output_path):
    doc = SimpleDocTemplate(output_path, pagesize=letter)
    elements = []
    styles = getSampleStyleSheet()

    # Title
    elements.append(Paragraph("Monthly Sales Report", styles['Title']))

    # Data table
    table_data = [['Product', 'Units', 'Revenue']]
    for item in data:
        table_data.append([item['product'], item['units'], f"${item['revenue']}"])

    table = Table(table_data)
    elements.append(table)

    doc.build(elements)

Use Case 3: PDF Merge and Split

from PyPDF2 import PdfMerger, PdfReader, PdfWriter

def merge_pdfs(pdf_list, output_path):
    merger = PdfMerger()
    for pdf in pdf_list:
        merger.append(pdf)
    merger.write(output_path)
    merger.close()

def split_pdf(input_path, output_dir):
    reader = PdfReader(input_path)
    for i, page in enumerate(reader.pages):
        writer = PdfWriter()
        writer.add_page(page)
        with open(f"{output_dir}/page_{i+1}.pdf", 'wb') as out:
            writer.write(out)

Use Case 4: Form Filling

from PyPDF2 import PdfReader, PdfWriter

def fill_pdf_form(template_path, data, output_path):
    reader = PdfReader(template_path)
    writer = PdfWriter()

    writer.append(reader)
    writer.update_page_form_field_values(
        writer.pages[0],
        data  # {'field_name': 'value', ...}
    )

    with open(output_path, 'wb') as out:
        writer.write(out)

OCR for Scanned PDFs

import pytesseract
from pdf2image import convert_from_path

def ocr_pdf(pdf_path):
    # Convert PDF pages to images
    images = convert_from_path(pdf_path)

    text = ""
    for image in images:
        text += pytesseract.image_to_string(image)

    return text

Integration with Automation Platforms

Connect PDF processing to n8n or Make.com:

Create a Python API endpoint (Flask/FastAPI)
Accept PDF upload or URL
Process and return extracted data
Call from automation workflow

Best Practices

Handle errors: PDFs vary wildly in structure
Validate outputs: Check extracted data makes sense
Use appropriate library: Match tool to task
Consider OCR: Scanned PDFs need different approach
Test with variety: PDFs from different sources behave differently

Need Custom PDF Automation?

Our Python automation team builds custom PDF processing solutions for businesses—from invoice extraction to report generation.