CRITICAL: You MUST complete these steps in order. Do not skip ahead to writing code.

If you need to fill out a PDF form, first check to see if the PDF has fillable form fields. Run this script from this file's directory: python scripts/check_fillable_fields <file.pdf>, and depending on the result go to either the "Fillable fields" or "Non-fillable fields" and follow those instructions.

Fillable fields

If the PDF has fillable form fields:

• Run this script from this file's directory: python scripts/extract_form_field_info.py <input.pdf> <field_info.json>. It will create a JSON file with a list of fields in this format:

[
  {
    "field_id": (unique ID for the field),
    "page": (page number, 1-based),
    "rect": ([left, bottom, right, top] bounding box in PDF coordinates, y=0 is the bottom of the page),
    "type": ("text", "checkbox", "radio_group", or "choice"),
  },
  // Checkboxes have "checked_value" and "unchecked_value" properties:
  {
    "field_id": (unique ID for the field),
    "page": (page number, 1-based),
    "type": "checkbox",
    "checked_value": (Set the field to this value to check the checkbox),
    "unchecked_value": (Set the field to this value to uncheck the checkbox),
  },
  // Radio groups have a "radio_options" list with the possible choices.
  {
    "field_id": (unique ID for the field),
    "page": (page number, 1-based),
    "type": "radio_group",
    "radio_options": [
      {
        "value": (set the field to this value to select this radio option),
        "rect": (bounding box for the radio button for this option)
      },
      // Other radio options
    ]
  },
  // Multiple choice fields have a "choice_options" list with the possible choices:
  {
    "field_id": (unique ID for the field),
    "page": (page number, 1-based),
    "type": "choice",
    "choice_options": [
      {
        "value": (set the field to this value to select this option),
        "text": (display text of the option)
      },
      // Other choice options
    ],
  }
]

• Convert the PDF to PNGs (one image for each page) with this script (run from this file's directory): python scripts/convert_pdf_to_images.py <file.pdf> <output_directory> Then analyze the images to determine the purpose of each form field (make sure to convert the bounding box PDF coordinates to image coordinates).
• Create a field_values.json file in this format with the values to be entered for each field:

[
  {
    "field_id": "last_name", // Must match the field_id from `extract_form_field_info.py`
    "description": "The user's last name",
    "page": 1, // Must match the "page" value in field_info.json
    "value": "Simpson"
  },
  {
    "field_id": "Checkbox12",
    "description": "Checkbox to be checked if the user is 18 or over",
    "page": 1,
    "value": "/On" // If this is a checkbox, use its "checked_value" value to check it. If it's a radio button group, use one of the "value" values in "radio_options".
  },
  // more fields
]

• Run the fill_fillable_fields.py script from this file's directory to create a filled-in PDF: python scripts/fill_fillable_fields.py <input pdf> <field_values.json> <output pdf> This script will verify that the field IDs and values you provide are valid; if it prints error messages, correct the appropriate fields and try again.

Non-fillable fields

If the PDF doesn't have fillable form fields, you'll add text annotations. First try to extract coordinates from the PDF structure (more accurate), then fall back to visual estimation if needed.

Step 1: Try Structure Extraction First

Run this script to extract text labels, lines, and checkboxes with their exact PDF coordinates: python scripts/extract_form_structure.py <input.pdf> form_structure.json

This creates a JSON file containing:

• labels: Every text element with exact coordinates (x0, top, x1, bottom in PDF points)
• lines: Horizontal lines that define row boundaries
• checkboxes: Small square rectangles that are checkboxes (with center coordinates)
• row_boundaries: Row top/bottom positions calculated from horizontal lines

Check the results: If form_structure.json has meaningful labels (text elements that correspond to form fields), use Approach A: Structure-Based Coordinates. If the PDF is scanned/image-based and has few or no labels, use Approach B: Visual Estimation.

Approach A: Structure-Based Coordinates (Preferred)

Use this when extract_form_structure.py found text labels in the PDF.

A.1: Analyze the Structure

Read form_structure.json and identify:

1. Label groups: Adjacent text elements that form a single label (e.g., "Last" + "Name")
2. Row structure: Labels with similar top values are in the same row
3. Field columns: Entry areas start after label ends (x0 = label.x1 + gap)
4. Checkboxes: Use the checkbox coordinates directly from the structure

Coordinate system: PDF coordinates where y=0 is at TOP of page, y increases downward.

A.2: Check for Missing Elements

The structure extraction may not detect all form elements. Common cases:

• Circular checkboxes: Only square rectangles are detected as checkboxes
• Complex graphics: Decorative elements or non-standard form controls
• Faded or light-colored elements: May not be extracted

If you see form fields in the PDF images that aren't in form_structure.json, you'll need to use visual analysis for those specific fields (see "Hybrid Approach" below).

A.3: Create fields.json with PDF Coordinates

For each field, calculate entry coordinates from the extracted structure:

Text fields:

• entry x0 = label x1 + 5 (small gap after label)
• entry x1 = next label's x0, or row boundary
• entry top = same as label top
• entry bottom = row boundary line below, or label bottom + row_height

Checkboxes:

• Use the checkbox rectangle coordinates directly from form_structure.json
• entry_bounding_box = [checkbox.x0, checkbox.top, checkbox.x1, checkbox.bottom]

Create fields.json using pdf_width and pdf_height (signals PDF coordinates):

{
  "pages": [
    {"page_number": 1, "pdf_width": 612, "pdf_height": 792}
  ],
  "form_fields": [
    {
      "page_number": 1,
      "description": "Last name entry field",
      "field_label": "Last Name",
      "label_bounding_box": [43, 63, 87, 73],
      "entry_bounding_box": [92, 63, 260, 79],
      "entry_text": {"text": "Smith", "font_size": 10}
    },
    {
      "page_number": 1,
      "description": "US Citizen Yes checkbox",
      "field_label": "Yes",
      "label_bounding_box": [260, 200, 280, 210],
      "entry_bounding_box": [285, 197, 292, 205],
      "entry_text": {"text": "X"}
    }
  ]
}

Important: Use pdf_width/pdf_height and coordinates directly from form_structure.json.

A.4: Validate Bounding Boxes

Before filling, check your bounding boxes for errors: python scripts/check_bounding_boxes.py fields.json

This checks for intersecting bounding boxes and entry boxes that are too small for the font size. Fix any reported errors before filling.

Approach B: Visual Estimation (Fallback)

Use this when the PDF is scanned/image-based and structure extraction found no usable text labels (e.g., all text shows as "(cid:X)" patterns).

B.1: Convert PDF to Images

python scripts/convert_pdf_to_images.py <input.pdf> <images_dir/>

B.2: Initial Field Identification

Examine each page image to identify form sections and get rough estimates of field locations:

• Form field labels and their approximate positions
• Entry areas (lines, boxes, or blank spaces for text input)
• Checkboxes and their approximate locations

For each field, note approximate pixel coordinates (they don't need to be precise yet).

B.3: Zoom Refinement (CRITICAL for accuracy)

For each field, crop a region around the estimated position to refine coordinates precisely.

Create a zoomed crop using ImageMagick:

magick <page_image> -crop <width>x<height>+<x>+<y> +repage <crop_output.png>

Where:

• <x>, <y> = top-left corner of crop region (use your rough estimate minus padding)
• <width>, <height> = size of crop region (field area plus ~50px padding on each side)

Example: To refine a "Name" field estimated around (100, 150):

magick images_dir/page_1.png -crop 300x80+50+120 +repage crops/name_field.png

(Note: if the magick command isn't available, try convert with the same arguments).

Examine the cropped image to determine precise coordinates:

1. Identify the exact pixel where the entry area begins (after the label)
2. Identify where the entry area ends (before next field or edge)
3. Identify the top and bottom of the entry line/box

Convert crop coordinates back to full image coordinates:

• full_x = crop_x + crop_offset_x
• full_y = crop_y + crop_offset_y

Example: If the crop started at (50, 120) and the entry box starts at (52, 18) within the crop:

• entry_x0 = 52 + 50 = 102
• entry_top = 18 + 120 = 138

Repeat for each field, grouping nearby fields into single crops when possible.

B.4: Create fields.json with Refined Coordinates

Create fields.json using image_width and image_height (signals image coordinates):

{
  "pages": [
    {"page_number": 1, "image_width": 1700, "image_height": 2200}
  ],
  "form_fields": [
    {
      "page_number": 1,
      "description": "Last name entry field",
      "field_label": "Last Name",
      "label_bounding_box": [120, 175, 242, 198],
      "entry_bounding_box": [255, 175, 720, 218],
      "entry_text": {"text": "Smith", "font_size": 10}
    }
  ]
}

Important: Use image_width/image_height and the refined pixel coordinates from the zoom analysis.

B.5: Validate Bounding Boxes

Before filling, check your bounding boxes for errors: python scripts/check_bounding_boxes.py fields.json

This checks for intersecting bounding boxes and entry boxes that are too small for the font size. Fix any reported errors before filling.

Hybrid Approach: Structure + Visual

Use this when structure extraction works for most fields but misses some elements (e.g., circular checkboxes, unusual form controls).

1. Use Approach A for fields that were detected in form_structure.json
2. Convert PDF to images for visual analysis of missing fields
3. Use zoom refinement (from Approach B) for the missing fields
4. Combine coordinates: For fields from structure extraction, use pdf_width/pdf_height. For visually-estimated fields, you must convert image coordinates to PDF coordinates:
   • pdf_x = image_x * (pdf_width / image_width)
   • pdf_y = image_y * (pdf_height / image_height)
5. Use a single coordinate system in fields.json - convert all to PDF coordinates with pdf_width/pdf_height

Step 2: Validate Before Filling

Always validate bounding boxes before filling: python scripts/check_bounding_boxes.py fields.json

This checks for:

• Intersecting bounding boxes (which would cause overlapping text)
• Entry boxes that are too small for the specified font size

Fix any reported errors in fields.json before proceeding.

Step 3: Fill the Form

The fill script auto-detects the coordinate system and handles conversion: python scripts/fill_pdf_form_with_annotations.py <input.pdf> fields.json <output.pdf>

Step 4: Verify Output

Convert the filled PDF to images and verify text placement: python scripts/convert_pdf_to_images.py <output.pdf> <verify_images/>

If text is mispositioned:

• Approach A: Check that you're using PDF coordinates from form_structure.json with pdf_width/pdf_height
• Approach B: Check that image dimensions match and coordinates are accurate pixels
• Hybrid: Ensure coordinate conversions are correct for visually-estimated fields

PDF Processing Advanced Reference

This document contains advanced PDF processing features, detailed examples, and additional libraries not covered in the main skill instructions.

pypdfium2 Library (Apache/BSD License)

Overview

pypdfium2 is a Python binding for PDFium (Chromium's PDF library). It's excellent for fast PDF rendering, image generation, and serves as a PyMuPDF replacement.

Render PDF to Images

import pypdfium2 as pdfium
from PIL import Image

# Load PDF
pdf = pdfium.PdfDocument("document.pdf")

# Render page to image
page = pdf[0]  # First page
bitmap = page.render(
    scale=2.0,  # Higher resolution
    rotation=0  # No rotation
)

# Convert to PIL Image
img = bitmap.to_pil()
img.save("page_1.png", "PNG")

# Process multiple pages
for i, page in enumerate(pdf):
    bitmap = page.render(scale=1.5)
    img = bitmap.to_pil()
    img.save(f"page_{i+1}.jpg", "JPEG", quality=90)

Extract Text with pypdfium2

import pypdfium2 as pdfium

pdf = pdfium.PdfDocument("document.pdf")
for i, page in enumerate(pdf):
    text = page.get_text()
    print(f"Page {i+1} text length: {len(text)} chars")

JavaScript Libraries

pdf-lib (MIT License)

pdf-lib is a powerful JavaScript library for creating and modifying PDF documents in any JavaScript environment.

Load and Manipulate Existing PDF

import { PDFDocument } from 'pdf-lib';
import fs from 'fs';

async function manipulatePDF() {
    // Load existing PDF
    const existingPdfBytes = fs.readFileSync('input.pdf');
    const pdfDoc = await PDFDocument.load(existingPdfBytes);

    // Get page count
    const pageCount = pdfDoc.getPageCount();
    console.log(`Document has ${pageCount} pages`);

    // Add new page
    const newPage = pdfDoc.addPage([600, 400]);
    newPage.drawText('Added by pdf-lib', {
        x: 100,
        y: 300,
        size: 16
    });

    // Save modified PDF
    const pdfBytes = await pdfDoc.save();
    fs.writeFileSync('modified.pdf', pdfBytes);
}

Create Complex PDFs from Scratch

import { PDFDocument, rgb, StandardFonts } from 'pdf-lib';
import fs from 'fs';

async function createPDF() {
    const pdfDoc = await PDFDocument.create();

    // Add fonts
    const helveticaFont = await pdfDoc.embedFont(StandardFonts.Helvetica);
    const helveticaBold = await pdfDoc.embedFont(StandardFonts.HelveticaBold);

    // Add page
    const page = pdfDoc.addPage([595, 842]); // A4 size
    const { width, height } = page.getSize();

    // Add text with styling
    page.drawText('Invoice #12345', {
        x: 50,
        y: height - 50,
        size: 18,
        font: helveticaBold,
        color: rgb(0.2, 0.2, 0.8)
    });

    // Add rectangle (header background)
    page.drawRectangle({
        x: 40,
        y: height - 100,
        width: width - 80,
        height: 30,
        color: rgb(0.9, 0.9, 0.9)
    });

    // Add table-like content
    const items = [
        ['Item', 'Qty', 'Price', 'Total'],
        ['Widget', '2', '$50', '$100'],
        ['Gadget', '1', '$75', '$75']
    ];

    let yPos = height - 150;
    items.forEach(row => {
        let xPos = 50;
        row.forEach(cell => {
            page.drawText(cell, {
                x: xPos,
                y: yPos,
                size: 12,
                font: helveticaFont
            });
            xPos += 120;
        });
        yPos -= 25;
    });

    const pdfBytes = await pdfDoc.save();
    fs.writeFileSync('created.pdf', pdfBytes);
}

Advanced Merge and Split Operations

import { PDFDocument } from 'pdf-lib';
import fs from 'fs';

async function mergePDFs() {
    // Create new document
    const mergedPdf = await PDFDocument.create();

    // Load source PDFs
    const pdf1Bytes = fs.readFileSync('doc1.pdf');
    const pdf2Bytes = fs.readFileSync('doc2.pdf');

    const pdf1 = await PDFDocument.load(pdf1Bytes);
    const pdf2 = await PDFDocument.load(pdf2Bytes);

    // Copy pages from first PDF
    const pdf1Pages = await mergedPdf.copyPages(pdf1, pdf1.getPageIndices());
    pdf1Pages.forEach(page => mergedPdf.addPage(page));

    // Copy specific pages from second PDF (pages 0, 2, 4)
    const pdf2Pages = await mergedPdf.copyPages(pdf2, [0, 2, 4]);
    pdf2Pages.forEach(page => mergedPdf.addPage(page));

    const mergedPdfBytes = await mergedPdf.save();
    fs.writeFileSync('merged.pdf', mergedPdfBytes);
}

pdfjs-dist (Apache License)

PDF.js is Mozilla's JavaScript library for rendering PDFs in the browser.

Basic PDF Loading and Rendering

import * as pdfjsLib from 'pdfjs-dist';

// Configure worker (important for performance)
pdfjsLib.GlobalWorkerOptions.workerSrc = './pdf.worker.js';

async function renderPDF() {
    // Load PDF
    const loadingTask = pdfjsLib.getDocument('document.pdf');
    const pdf = await loadingTask.promise;

    console.log(`Loaded PDF with ${pdf.numPages} pages`);

    // Get first page
    const page = await pdf.getPage(1);
    const viewport = page.getViewport({ scale: 1.5 });

    // Render to canvas
    const canvas = document.createElement('canvas');
    const context = canvas.getContext('2d');
    canvas.height = viewport.height;
    canvas.width = viewport.width;

    const renderContext = {
        canvasContext: context,
        viewport: viewport
    };

    await page.render(renderContext).promise;
    document.body.appendChild(canvas);
}

Extract Text with Coordinates

import * as pdfjsLib from 'pdfjs-dist';

async function extractText() {
    const loadingTask = pdfjsLib.getDocument('document.pdf');
    const pdf = await loadingTask.promise;

    let fullText = '';

    // Extract text from all pages
    for (let i = 1; i <= pdf.numPages; i++) {
        const page = await pdf.getPage(i);
        const textContent = await page.getTextContent();

        const pageText = textContent.items
            .map(item => item.str)
            .join(' ');

        fullText += `\n--- Page ${i} ---\n${pageText}`;

        // Get text with coordinates for advanced processing
        const textWithCoords = textContent.items.map(item => ({
            text: item.str,
            x: item.transform[4],
            y: item.transform[5],
            width: item.width,
            height: item.height
        }));
    }

    console.log(fullText);
    return fullText;
}

Extract Annotations and Forms

import * as pdfjsLib from 'pdfjs-dist';

async function extractAnnotations() {
    const loadingTask = pdfjsLib.getDocument('annotated.pdf');
    const pdf = await loadingTask.promise;

    for (let i = 1; i <= pdf.numPages; i++) {
        const page = await pdf.getPage(i);
        const annotations = await page.getAnnotations();

        annotations.forEach(annotation => {
            console.log(`Annotation type: ${annotation.subtype}`);
            console.log(`Content: ${annotation.contents}`);
            console.log(`Coordinates: ${JSON.stringify(annotation.rect)}`);
        });
    }
}

Advanced Command-Line Operations

poppler-utils Advanced Features

Extract Text with Bounding Box Coordinates

# Extract text with bounding box coordinates (essential for structured data)
pdftotext -bbox-layout document.pdf output.xml

# The XML output contains precise coordinates for each text element

Advanced Image Conversion

# Convert to PNG images with specific resolution
pdftoppm -png -r 300 document.pdf output_prefix

# Convert specific page range with high resolution
pdftoppm -png -r 600 -f 1 -l 3 document.pdf high_res_pages

# Convert to JPEG with quality setting
pdftoppm -jpeg -jpegopt quality=85 -r 200 document.pdf jpeg_output

Extract Embedded Images

# Extract all embedded images with metadata
pdfimages -j -p document.pdf page_images

# List image info without extracting
pdfimages -list document.pdf

# Extract images in their original format
pdfimages -all document.pdf images/img

qpdf Advanced Features

Complex Page Manipulation

# Split PDF into groups of pages
qpdf --split-pages=3 input.pdf output_group_%02d.pdf

# Extract specific pages with complex ranges
qpdf input.pdf --pages input.pdf 1,3-5,8,10-end -- extracted.pdf

# Merge specific pages from multiple PDFs
qpdf --empty --pages doc1.pdf 1-3 doc2.pdf 5-7 doc3.pdf 2,4 -- combined.pdf

PDF Optimization and Repair

# Optimize PDF for web (linearize for streaming)
qpdf --linearize input.pdf optimized.pdf

# Remove unused objects and compress
qpdf --optimize-level=all input.pdf compressed.pdf

# Attempt to repair corrupted PDF structure
qpdf --check input.pdf
qpdf --fix-qdf damaged.pdf repaired.pdf

# Show detailed PDF structure for debugging
qpdf --show-all-pages input.pdf > structure.txt

Advanced Encryption

# Add password protection with specific permissions
qpdf --encrypt user_pass owner_pass 256 --print=none --modify=none -- input.pdf encrypted.pdf

# Check encryption status
qpdf --show-encryption encrypted.pdf

# Remove password protection (requires password)
qpdf --password=secret123 --decrypt encrypted.pdf decrypted.pdf

Advanced Python Techniques

pdfplumber Advanced Features

Extract Text with Precise Coordinates

import pdfplumber

with pdfplumber.open("document.pdf") as pdf:
    page = pdf.pages[0]
    
    # Extract all text with coordinates
    chars = page.chars
    for char in chars[:10]:  # First 10 characters
        print(f"Char: '{char['text']}' at x:{char['x0']:.1f} y:{char['y0']:.1f}")
    
    # Extract text by bounding box (left, top, right, bottom)
    bbox_text = page.within_bbox((100, 100, 400, 200)).extract_text()

Advanced Table Extraction with Custom Settings

import pdfplumber
import pandas as pd

with pdfplumber.open("complex_table.pdf") as pdf:
    page = pdf.pages[0]
    
    # Extract tables with custom settings for complex layouts
    table_settings = {
        "vertical_strategy": "lines",
        "horizontal_strategy": "lines",
        "snap_tolerance": 3,
        "intersection_tolerance": 15
    }
    tables = page.extract_tables(table_settings)
    
    # Visual debugging for table extraction
    img = page.to_image(resolution=150)
    img.save("debug_layout.png")

reportlab Advanced Features

Create Professional Reports with Tables

from reportlab.platypus import SimpleDocTemplate, Table, TableStyle, Paragraph
from reportlab.lib.styles import getSampleStyleSheet
from reportlab.lib import colors

# Sample data
data = [
    ['Product', 'Q1', 'Q2', 'Q3', 'Q4'],
    ['Widgets', '120', '135', '142', '158'],
    ['Gadgets', '85', '92', '98', '105']
]

# Create PDF with table
doc = SimpleDocTemplate("report.pdf")
elements = []

# Add title
styles = getSampleStyleSheet()
title = Paragraph("Quarterly Sales Report", styles['Title'])
elements.append(title)

# Add table with advanced styling
table = Table(data)
table.setStyle(TableStyle([
    ('BACKGROUND', (0, 0), (-1, 0), colors.grey),
    ('TEXTCOLOR', (0, 0), (-1, 0), colors.whitesmoke),
    ('ALIGN', (0, 0), (-1, -1), 'CENTER'),
    ('FONTNAME', (0, 0), (-1, 0), 'Helvetica-Bold'),
    ('FONTSIZE', (0, 0), (-1, 0), 14),
    ('BOTTOMPADDING', (0, 0), (-1, 0), 12),
    ('BACKGROUND', (0, 1), (-1, -1), colors.beige),
    ('GRID', (0, 0), (-1, -1), 1, colors.black)
]))
elements.append(table)

doc.build(elements)

Complex Workflows

Extract Figures/Images from PDF

Method 1: Using pdfimages (fastest)

# Extract all images with original quality
pdfimages -all document.pdf images/img

Method 2: Using pypdfium2 + Image Processing

import pypdfium2 as pdfium
from PIL import Image
import numpy as np

def extract_figures(pdf_path, output_dir):
    pdf = pdfium.PdfDocument(pdf_path)
    
    for page_num, page in enumerate(pdf):
        # Render high-resolution page
        bitmap = page.render(scale=3.0)
        img = bitmap.to_pil()
        
        # Convert to numpy for processing
        img_array = np.array(img)
        
        # Simple figure detection (non-white regions)
        mask = np.any(img_array != [255, 255, 255], axis=2)
        
        # Find contours and extract bounding boxes
        # (This is simplified - real implementation would need more sophisticated detection)
        
        # Save detected figures
        # ... implementation depends on specific needs

Batch PDF Processing with Error Handling

import os
import glob
from pypdf import PdfReader, PdfWriter
import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

def batch_process_pdfs(input_dir, operation='merge'):
    pdf_files = glob.glob(os.path.join(input_dir, "*.pdf"))
    
    if operation == 'merge':
        writer = PdfWriter()
        for pdf_file in pdf_files:
            try:
                reader = PdfReader(pdf_file)
                for page in reader.pages:
                    writer.add_page(page)
                logger.info(f"Processed: {pdf_file}")
            except Exception as e:
                logger.error(f"Failed to process {pdf_file}: {e}")
                continue
        
        with open("batch_merged.pdf", "wb") as output:
            writer.write(output)
    
    elif operation == 'extract_text':
        for pdf_file in pdf_files:
            try:
                reader = PdfReader(pdf_file)
                text = ""
                for page in reader.pages:
                    text += page.extract_text()
                
                output_file = pdf_file.replace('.pdf', '.txt')
                with open(output_file, 'w', encoding='utf-8') as f:
                    f.write(text)
                logger.info(f"Extracted text from: {pdf_file}")
                
            except Exception as e:
                logger.error(f"Failed to extract text from {pdf_file}: {e}")
                continue

Advanced PDF Cropping

from pypdf import PdfWriter, PdfReader

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

# Crop page (left, bottom, right, top in points)
page = reader.pages[0]
page.mediabox.left = 50
page.mediabox.bottom = 50
page.mediabox.right = 550
page.mediabox.top = 750

writer.add_page(page)
with open("cropped.pdf", "wb") as output:
    writer.write(output)

Performance Optimization Tips

1. For Large PDFs

• Use streaming approaches instead of loading entire PDF in memory
• Use qpdf --split-pages for splitting large files
• Process pages individually with pypdfium2

2. For Text Extraction

• pdftotext -bbox-layout is fastest for plain text extraction
• Use pdfplumber for structured data and tables
• Avoid pypdf.extract_text() for very large documents

3. For Image Extraction

• pdfimages is much faster than rendering pages
• Use low resolution for previews, high resolution for final output

4. For Form Filling

• pdf-lib maintains form structure better than most alternatives
• Pre-validate form fields before processing

5. Memory Management

# Process PDFs in chunks
def process_large_pdf(pdf_path, chunk_size=10):
    reader = PdfReader(pdf_path)
    total_pages = len(reader.pages)
    
    for start_idx in range(0, total_pages, chunk_size):
        end_idx = min(start_idx + chunk_size, total_pages)
        writer = PdfWriter()
        
        for i in range(start_idx, end_idx):
            writer.add_page(reader.pages[i])
        
        # Process chunk
        with open(f"chunk_{start_idx//chunk_size}.pdf", "wb") as output:
            writer.write(output)

Troubleshooting Common Issues

Encrypted PDFs

# Handle password-protected PDFs
from pypdf import PdfReader

try:
    reader = PdfReader("encrypted.pdf")
    if reader.is_encrypted:
        reader.decrypt("password")
except Exception as e:
    print(f"Failed to decrypt: {e}")

Corrupted PDFs

# Use qpdf to repair
qpdf --check corrupted.pdf
qpdf --replace-input corrupted.pdf

Text Extraction Issues

# Fallback to OCR for scanned PDFs
import pytesseract
from pdf2image import convert_from_path

def extract_text_with_ocr(pdf_path):
    images = convert_from_path(pdf_path)
    text = ""
    for i, image in enumerate(images):
        text += pytesseract.image_to_string(image)
    return text

License Information

• pypdf: BSD License
• pdfplumber: MIT License
• pypdfium2: Apache/BSD License
• reportlab: BSD License
• poppler-utils: GPL-2 License
• qpdf: Apache License
• pdf-lib: MIT License
• pdfjs-dist: Apache License