PDF Content Extraction: Pdftotext vs Docling — Choosing the Right Tool for Different Scenarios

In modern AI application development, processing PDF documents is a common yet challenging task. Whether building RAG (Retrieval Augmented Generation) systems, document analysis tools, or knowledge management platforms, choosing the right PDF content extraction tool is crucial. Today, we will compare two mainstream solutions: the traditional pdftotext (part of the Poppler toolset) and the emerging Docling framework, to help you make an informed choice based on your actual needs.

Tool Overview

pdftotext (Poppler)

pdftotext is a time-tested command-line tool that is part of the Poppler PDF rendering library. Its design philosophy is simple and direct: quickly convert PDF documents to plain text, removing all formatting and structural information.

Key Features:

• Extremely fast processing speed
• Pure text output with zero dependencies
• Lightweight, stable, and reliable
• Cross-platform support (Linux, macOS, Windows)

Docling

Docling is a modern document processing framework developed by IBM Research Zurich, designed specifically for the era of generative AI. It is not just a PDF extraction tool, but a complete document understanding system.

Key Features:

• Advanced PDF understanding capabilities (layout, reading order, table structure)
• Preserves document structure and formatting information
• Supports multiple document formats (PDF, DOCX, PPTX, XLSX, etc.)
• Native integration with AI frameworks like LangChain, LlamaIndex
• Provides recognition and extraction of images, tables, and formulas
• Supports OCR processing for scanned documents

Usage Scenario Comparison

Scenario 1: LLM Context Input → Choose pdftotext

When your goal is to provide context to large language models, pdftotext is the ideal choice.

Why?

1. Speed Advantage: pdftotext’s processing speed is typically 10-100 times faster than complex parsers. This speed difference is crucial when you need to process a large number of documents or respond to user queries in real-time.

2. Avoid Multimodal Complexity: Pure text output means you can use cheaper, faster pure text models, without requiring multimodal models that support image input.

3. Token Efficiency: After removing formatting tags and extra whitespace, the text is more compact, allowing more substantive content to fit within a limited context window.

4. Consistency: pdftotext’s output format is stable and predictable, facilitating subsequent text processing and vectorization.

Practical Example:

# Quickly extract PDF text
pdftotext document.pdf - | head -n 50

# Batch process document library
for file in documents/*.pdf; do
    pdftotext "$file" "text_output/$(basename "$file" .pdf).txt"
done

Typical Applications:

• Document index building for RAG systems
• Populating vector databases for semantic search
• Document question-answering systems
• Large-scale document analysis (e.g., legal document review)

Scenario 2: Human-Readable Document Presentation → Choose Docling

When the extracted content needs to be read by humans or requires the preservation of document structure, Docling offers clear advantages.

Why?

1. Structure Preservation: Docling can identify and preserve heading levels, paragraph structures, lists, tables, etc., generating Markdown or HTML output with good readability.

2. Visual Element Extraction: It can extract and save visual content such as charts, images, and formulas, which carry critical information in many documents.

3. Table Understanding: Docling can accurately identify the row and column structure of complex tables, whereas pdftotext usually converts tables into an unintelligible text stream.

4. Multimodal Output: The generated DoclingDocument format contains multiple layers of information, including text, images, and metadata, supporting rich downstream applications.

Practical Example:

from docling.document_converter import DocumentConverter

# Initialize converter
converter = DocumentConverter()

# Convert PDF and export to Markdown
result = converter.convert("report.pdf")
markdown_content = result.document.export_to_markdown()

# Save results
with open("report.md", "w", encoding="utf-8") as f:
    f.write(markdown_content)

# Or export to JSON to preserve full structure
json_content = result.document.export_to_json()

Typical Applications:

• Preview functionality for document management systems
• Online display of academic papers
• Report generation and format conversion
• Processing technical documents that require chart preservation
• Data extraction (extracting structured data from complex tables)

Performance Comparison

Hybrid Strategy: Combining Both

In actual projects, you can flexibly combine the two tools based on different needs:

Strategy 1: Layered Processing

# First layer: Fast text indexing (for search)
import subprocess

def quick_index(pdf_path):
    """Quickly build search index using pdftotext"""
    result = subprocess.run(
        ['pdftotext', pdf_path, '-'],
        capture_output=True,
        text=True
    )
    return result.stdout

# Second layer: Detailed parsing on demand (for display)
from docling.document_converter import DocumentConverter

def detailed_parse(pdf_path):
    """Use Docling for detailed parsing when needed"""
    converter = DocumentConverter()
    return converter.convert(pdf_path)

Strategy 2: Document Type Routing

def process_document(pdf_path, doc_type):
    """Choose processing method based on document type"""
    if doc_type in ['contract', 'agreement', 'plain_text']:
        # Use pdftotext for simple documents
        return extract_with_pdftotext(pdf_path)
    elif doc_type in ['report', 'research_paper', 'presentation']:
        # Use Docling for complex documents
        return extract_with_docling(pdf_path)

Practical Advice

Choose pdftotext if:

• Building RAG systems or vector databases
• Need to process a large number of documents (>1000)
• High real-time requirements
• Limited resources in the operating environment
• Document structure is simple, mainly continuous text

Choose Docling if:

• Need to display document content to users
• Documents contain important tables, charts, or formulas
• Need to extract structured data
• Building document management or knowledge base systems
• Processing scanned PDFs (requires OCR)
• Need to integrate with frameworks like LangChain, LlamaIndex

Summary

pdftotext and Docling represent two philosophies of PDF processing:

• pdftotext: Minimalism, focusing on fast, reliable text extraction, ideal for LLM applications.
• Docling: Comprehensive, pursuing complete document understanding, suitable for scenarios requiring the preservation of structure and visual elements.

In AI application development, understanding the advantages and limitations of these two tools and choosing the appropriate solution based on specific needs will help you build more efficient and practical document processing systems. For many projects, a hybrid approach—using pdftotext for quick indexing and Docling for documents requiring detailed display—may be the best practice.

Further Reading

• Docling GitHub Repository
• Docling Technical Report
• Poppler Official Documentation