PDF Processing

PDF Processing gives you programmatic control to read, extract, merge, split, and manipulate PDF documents—automating what would take hours of manual work through desktop applications.

What Is This?

• Handles reading, extracting text and tables, merging, splitting, rotating, and encrypting PDFs using Python libraries (pypdf and pdfplumber)
• Preserves layout and structure when extracting tables and text from PDF reports
• Supports OCR on scanned documents, form filling, watermarking, and image extraction

Why Use It?

• Automate repetitive tasks: Process dozens or hundreds of PDFs consistently without manual intervention
• Extract structured data: Preserve table formatting and convert PDF data to analysis-ready formats like CSV
• Make scanned PDFs searchable: OCR converts image-based PDFs into text-based, indexed documents
• Batch handle complex operations: Merge, split, rotate, and encrypt files in minutes instead of days

How to Use It?

Extract text from a PDF:

from pypdf import PdfReader

reader = PdfReader("document.pdf")
text = ""
for page in reader.pages:
    text += page.extract_text()

Merge multiple PDFs:

from pypdf import PdfWriter, PdfReader

writer = PdfWriter()
for pdf_file in ["doc1.pdf", "doc2.pdf"]:
    reader = PdfReader(pdf_file)
    for page in reader.pages:
        writer.add_page(page)

with open("merged.pdf", "wb") as output:
    writer.write(output)

Extract tables:

import pdfplumber

with pdfplumber.open("report.pdf") as pdf:
    for page in pdf.pages:
        tables = page.extract_tables()
        for table in tables:
            for row in table:
                print(row)

Split into individual pages:

reader = PdfReader("input.pdf")
for i, page in enumerate(reader.pages):
    writer = PdfWriter()
    writer.add_page(page)
    with open(f"page_{i+1}.pdf", "wb") as output:
        writer.write(output)

When to Use It?

• Extracting tables from financial or business reports for database import
• Merging case documents or consolidating files from multiple sources
• Converting scanned archives into searchable, full-text indexed collections

Important Notes

• Text extraction quality depends on PDF structure; scanned images extract poorly without OCR
• Encrypted PDFs require passwords; some advanced features like complex annotations may not be supported
• Test extraction logic on sample files before batch processing large volumes

Try It in Happycapy

1. Open Happycapy in your browser (no install or signup needed to try).
2. Describe what you want in one sentence, for example: "Merge these three PDF reports into a single document" or "Extract all tables from this sales report and convert to CSV."
3. Get your result—the PDF Processing skill handles the operation and returns your processed file.