Quick Start

Python
import PyPDF2
import pandas as pd
from fpdf import FPDF

# Extract text from PDF
with open('document.pdf', 'rb') as file:
    reader = PyPDF2.PdfReader(file)
    text = ""
    for page in reader.pages:
        text += page.extract_text()
    print(text)

Workflow

Progress:

• Identify PDF type (text-based, scanned, form)
• Choose appropriate extraction method
• Process content (text, tables, forms)
• Output in desired format
• Validate results

1. Install Dependencies

   Bash
   pip install PyPDF2 pandas tabula-py fpdf2 pytesseract pdf2image

2. Analyze PDF Structure

   Python
   reader = PyPDF2.PdfReader('file.pdf')
   print(f"Pages: {len(reader.pages)}")
   print(f"Encrypted: {reader.is_encrypted}")

3. Choose Processing Method

   • Text-based: PyPDF2/pdfplumber
   • Scanned: OCR with pytesseract
   • Tables: tabula-py
   • Forms: PyPDF2 form handling

Examples

Example 1: Merge PDFs Input: Multiple PDF files in directory

Python
import PyPDF2
import glob

merger = PyPDF2.PdfMerger()
for pdf in sorted(glob.glob("*.pdf")):
    merger.append(pdf)
merger.write("merged.pdf")
merger.close()

Output: Single merged PDF file

Example 2: Extract Tables Input: PDF with tabular data

Python
import tabula
df = tabula.read_pdf("report.pdf", pages="all")
df[0].to_csv("extracted_table.csv", index=False)

Output: CSV file with table data

Example 3: Split PDF Input: Multi-page PDF

Python
reader = PyPDF2.PdfReader("document.pdf")
for i, page in enumerate(reader.pages):
    writer = PyPDF2.PdfWriter()
    writer.add_page(page)
    with open(f"page_{i+1}.pdf", "wb") as output:
        writer.write(output)

Output: Individual PDF files per page

Example 4: OCR Scanned PDF Input: Image-based PDF

Python
from pdf2image import convert_from_path
import pytesseract

images = convert_from_path("scanned.pdf")
text = ""
for img in images:
    text += pytesseract.image_to_string(img)

Output: Extracted text from scanned document

Example 5: Fill PDF Forms Input: PDF form with fillable fields

Python
reader = PyPDF2.PdfReader("form.pdf")
writer = PyPDF2.PdfWriter()
writer.clone_reader_document_root(reader)

writer.update_page_form_field_values(
    writer.pages[0], {"field_name": "New Value"}
)
with open("filled_form.pdf", "wb") as output:
    writer.write(output)

Output: PDF with populated form fields

Best Practices

• Use pdfplumber for complex text extraction - Better layout preservation than PyPDF2
• Batch process with pathlib - Handle multiple files efficiently
• Set OCR language explicitly - pytesseract.image_to_string(img, lang='eng')
• Handle encrypted PDFs - Check is_encrypted before processing
• Preserve metadata - Copy document info when merging/splitting
• Use temporary files for large operations - Avoid memory issues

Common Pitfalls

• Don't assume text extraction always works - Some PDFs are images
• Don't ignore PDF permissions - Check if extraction is allowed
• Don't process without validation - Verify page count and structure first
• Don't hardcode page numbers - Use dynamic page detection
• Don't forget to close file handles - Use context managers (with statements)
• Don't mix coordinate systems - PDF coordinates start bottom-left, not top-left