run2_pdf-extraction

PDF Text Extraction Skill (Improved)

Overview

Extract text from PDF files with improved robustness, handling corrupt PDFs gracefully. Multiple extraction strategies ensure maximum content recovery.

Installation

pip install pdfplumber pypdf pymupdf -q

Usage Examples

Primary Strategy: pdfplumber (Most Reliable)

import pdfplumber
import PyPDF2

def extract_pdf_content_robust(pdf_path, max_pages=5):
    """
    Extract text from PDF with fallback strategies.
    Tries pdfplumber first, then falls back to PyPDF2.
    """
    text = ""

    # Strategy 1: pdfplumber (best for modern PDFs)
    try:
        with pdfplumber.open(pdf_path) as pdf:
            for i, page in enumerate(pdf.pages[:max_pages]):
                extracted = page.extract_text()
                if extracted:
                    text += extracted + " "
        if len(text.strip()) > 100:  # If we got substantial text
            return text
    except Exception as e:
        pass  # Try fallback

    # Strategy 2: PyPDF2 (fallback for problematic PDFs)
    try:
        with open(pdf_path, 'rb') as f:
            reader = PyPDF2.PdfReader(f)
            for page in reader.pages[:max_pages]:
                text += page.extract_text() + " "
        if len(text.strip()) > 100:
            return text
    except Exception as e:
        pass

    return text or ""

Optimized Extraction for Academic Papers

def extract_paper_metadata(pdf_path, max_pages=3):
    """
    Extract key content from academic papers:
    - Title (usually on first page)
    - Abstract (usually first 3 pages)
    - Keywords (sometimes in abstract area)
    """
    try:
        with pdfplumber.open(pdf_path) as pdf:
            # Extract first 3 pages which contain title and abstract
            text = ""
            for page in pdf.pages[:max_pages]:
                text += page.extract_text() or ""
            return text
    except:
        return ""

Key Improvements

• Dual extraction strategy: pdfplumber + PyPDF2 fallback
• Robustness: Handles corrupt/damaged PDFs gracefully
• Academic focus: Optimized for extracting paper abstracts and titles
• Efficiency: Returns early if sufficient text extracted
• Error isolation: Failures in one method don't prevent trying alternatives

Best Practices

• For academic papers, first 3-5 pages contain most essential information
• Title and abstract are usually in first 2 pages
• Some PDFs are scanned images - these need OCR (pymupdf with OCR)
• Check text length to verify successful extraction
• Handle encoding issues gracefully