Pdf

PDF Processing Guide

Overview

This guide covers essential PDF processing operations using Python libraries and command-line tools. For advanced features, JavaScript libraries, and detailed examples, see reference.md. If you need to fill out a PDF form, read forms.md and follow its instructions.

Quick Start

from pypdf import PdfReader, PdfWriter

# Read a PDF
reader = PdfReader("document.pdf")
print(f"Pages: {len(reader.pages)}")

# Extract text
text = ""
for page in reader.pages:
    text += page.extract_text()

Python Libraries

pypdf - Basic Operations

Merge PDFs

from pypdf import PdfWriter, PdfReader

writer = PdfWriter()
for pdf_file in ["doc1.pdf", "doc2.pdf", "doc3.pdf"]:
    reader = PdfReader(pdf_file)
    for page in reader.pages:
        writer.add_page(page)

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

Split PDF

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)

Extract Metadata

reader = PdfReader("document.pdf")
meta = reader.metadata
print(f"Title: {meta.title}")
print(f"Author: {meta.author}")
print(f"Subject: {meta.subject}")
print(f"Creator: {meta.creator}")

Rotate Pages

reader = PdfReader("input.pdf")
writer = PdfWriter()

page = reader.pages[0]
page.rotate(90)  # Rotate 90 degrees clockwise
writer.add_page(page)

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

pdfplumber - Text and Table Extraction

Extract Text with Layout

import pdfplumber

with pdfplumber.open("document.pdf") as pdf:
    for page in pdf.pages:
        text = page.extract_text()
        print(text)

Extract Tables

with pdfplumber.open("document.pdf") as pdf:
    for i, page in enumerate(pdf.pages):
        tables = page.extract_tables()
        for j, table in enumerate(tables):
            print(f"Table {j+1} on page {i+1}:")
            for row in table:
                print(row)

Advanced Table Extraction

import pandas as pd

with pdfplumber.open("document.pdf") as pdf:
    all_tables = []
    for page in pdf.pages:
        tables = page.extract_tables()
        for table in tables:
            if table:  # Check if table is not empty
                df = pd.DataFrame(table[1:], columns=table[0])
                all_tables.append(df)

# Combine all tables
if all_tables:
    combined_df = pd.concat(all_tables, ignore_index=True)
    combined_df.to_excel("extracted_tables.xlsx", index=False)

reportlab - Create PDFs

Basic PDF Creation

from reportlab.lib.pagesizes import letter
from reportlab.pdfgen import canvas

c = canvas.Canvas("hello.pdf", pagesize=letter)
width, height = letter

# Add text
c.drawString(100, height - 100, "Hello World!")
c.drawString(100, height - 120, "This is a PDF created with reportlab")

# Add a line
c.line(100, height - 140, 400, height - 140)

# Save
c.save()

Create PDF with Multiple Pages

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

doc = SimpleDocTemplate("report.pdf", pagesize=letter)
styles = getSampleStyleSheet()
story = []

# Add content
title = Paragraph("Report Title", styles['Title'])
story.append(title)
story.append(Spacer(1, 12))

body = Paragraph("This is the body of the report. " * 20, styles['Normal'])
story.append(body)
story.append(PageBreak())

# Page 2
story.append(Paragraph("Page 2", styles['Heading1']))
story.append(Paragraph("Content for page 2", styles['Normal']))

# Build PDF
doc.build(story)

Command-Line Tools

pdftotext (poppler-utils)

# Extract text
pdftotext input.pdf output.txt

# Extract text preserving layout
pdftotext -layout input.pdf output.txt

# Extract specific pages
pdftotext -f 1 -l 5 input.pdf output.txt  # Pages 1-5

qpdf

# Merge PDFs
qpdf --empty --pages file1.pdf file2.pdf -- merged.pdf

# Split pages
qpdf input.pdf --pages . 1-5 -- pages1-5.pdf
qpdf input.pdf --pages . 6-10 -- pages6-10.pdf

# Rotate pages
qpdf input.pdf output.pdf --rotate=+90:1  # Rotate page 1 by 90 degrees

# Remove password
qpdf --password=mypassword --decrypt encrypted.pdf decrypted.pdf

pdftk (if available)

# Merge
pdftk file1.pdf file2.pdf cat output merged.pdf

# Split
pdftk input.pdf burst

# Rotate
pdftk input.pdf rotate 1east output rotated.pdf

Common Tasks

Extract Text from Scanned PDFs

# Requires: pip install pytesseract pdf2image
import pytesseract
from pdf2image import convert_from_path

# Convert PDF to images
images = convert_from_path('scanned.pdf')

# OCR each page
text = ""
for i, image in enumerate(images):
    text += f"Page {i+1}:\n"
    text += pytesseract.image_to_string(image)
    text += "\n\n"

print(text)

Add Watermark

from pypdf import PdfReader, PdfWriter

# Create watermark (or load existing)
watermark = PdfReader("watermark.pdf").pages[0]

# Apply to all pages
reader = PdfReader("document.pdf")
writer = PdfWriter()

for page in reader.pages:
    page.merge_page(watermark)
    writer.add_page(page)

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

Extract Images

# Using pdfimages (poppler-utils)
pdfimages -j input.pdf output_prefix

# This extracts all images as output_prefix-000.jpg, output_prefix-001.jpg, etc.

Password Protection

from pypdf import PdfReader, PdfWriter

reader = PdfReader("input.pdf")
writer = PdfWriter()

for page in reader.pages:
    writer.add_page(page)

# Add password
writer.encrypt("userpassword", "ownerpassword")

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

Quick Reference

Task	Best Tool	Command/Code
Merge PDFs	pypdf	writer.add_page(page)
Split PDFs	pypdf	One page per file
Extract text	pdfplumber	page.extract_text()
Extract tables	pdfplumber	page.extract_tables()
Create PDFs	reportlab	Canvas or Platypus
Command line merge	qpdf	qpdf --empty --pages ...
OCR scanned PDFs	pytesseract	Convert to image first
Fill PDF forms	pdf-lib or pypdf (see forms.md)	See forms.md

Best Practices for Data Extraction from PDFs

Always Verify Against Source Documents

When extracting structured data (names, emails, qualifications, dates, etc.) from PDFs:

1. Read raw text first - Before writing extraction logic, extract and review the full text from each PDF to understand the actual formatting:

   import pdfplumber
   from pathlib import Path
   
   for pdf_path in Path("documents").glob("*.pdf"):
       print(f"\n{'='*60}\nFILE: {pdf_path.name}\n{'='*60}")
       with pdfplumber.open(pdf_path) as pdf:
           for page in pdf.pages:
               text = page.extract_text()
               if text:
                   print(text[:2000])  # Review first 2000 chars
   

2. Don't trust regex blindly - Automated regex patterns often fail due to:

   • Format variations: "Bachelor of Science in Finance" vs "B.Sc, Finance" vs "B.S. (Finance)"
   • Different section headers: "EDUCATION" vs "ACADEMICS" vs "Academic Qualification"
   • Non-standard punctuation: parentheses, commas, semicolons, dashes
   • Inline vs sectioned layouts

3. Verify extraction output - Always spot-check extracted data against the original PDF content. If automated extraction returns "Not found" or suspicious values, manually review the source.

Common Pitfalls in PDF Data Extraction

Problem	Cause	Solution
Missing data	Regex too narrow	Review raw text, broaden patterns
Garbled text	PDF uses custom fonts/encoding	Try OCR with pytesseract instead
Wrong data	Multiple documents in one PDF	Check for page breaks, multiple resumes
Truncated values	Field length limits	Remove arbitrary truncation
Calculated values wrong	Date parsing errors	Validate date ranges manually

Recommended Extraction Workflow

# Step 1: Extract and review raw text from ALL documents first
texts = {}
for pdf_path in pdf_files:
    with pdfplumber.open(pdf_path) as pdf:
        texts[pdf_path.name] = "\n".join(
            page.extract_text() or "" for page in pdf.pages
        )

# Step 2: Analyze actual formats present in the documents
# - What section headers are used?
# - How are degrees/dates/emails formatted?
# - Are there multiple records per document?

# Step 3: Build extraction logic based on observed patterns
# - Use flexible regex that handles variations
# - Include fallback patterns
# - Log what was matched vs not matched

# Step 4: Verify results against source
# - Spot check 10-20% of extractions manually
# - Investigate any "Not found" results
# - Cross-reference suspicious values

Example: Flexible Education Extraction

import re

def extract_education(text):
    """Extract educational qualifications with flexible pattern matching."""
    education = []
    
    # Multiple patterns to catch format variations
    patterns = [
        # "Bachelor of Science in Finance"
        r"(Bachelor'?s?|Master'?s?|Doctor\w*|Associate'?s?)\s+(?:of\s+)?(\w+)(?:\s+(?:in|of)\s+[\w\s,]+)?",
        # "B.Sc, Finance" or "B.S. (Finance)"
        r"(B\.?S\.?c?|M\.?S\.?c?|B\.?A\.?|M\.?A\.?|M\.?B\.?A\.?|Ph\.?D\.?|LL\.?B\.?|LL\.?M\.?)[\s,.\(]+([A-Za-z\s]+)",
        # "PhD (Economics)"
        r"(PhD|MBA|LLB|LLM|CAIIB|CPA|CFA|CFM)\s*\(?([A-Za-z\s]*)\)?",
    ]
    
    for pattern in patterns:
        for match in re.finditer(pattern, text, re.IGNORECASE):
            edu_text = match.group(0).strip()
            if edu_text and len(edu_text) > 2:
                # Avoid duplicates
                if not any(edu_text.lower() in e.lower() for e in education):
                    education.append(edu_text)
    
    return education if education else ["Not specified - VERIFY MANUALLY"]

Next Steps

• For advanced pypdfium2 usage, see reference.md
• For JavaScript libraries (pdf-lib), see reference.md
• If you need to fill out a PDF form, follow the instructions in forms.md
• For troubleshooting guides, see reference.md