pdftext

name	pdftext
description	Extract text from PDFs for LLM consumption using AI-powered or traditional tools. Use when converting academic PDFs to markdown, extracting structured content (headers/tables/lists), batch processing research papers, preparing PDFs for RAG systems, or when mentions of "pdf extraction", "pdf to text", "pdf to markdown", "docling", "pymupdf", "pdfplumber" appear. Provides Docling (AI-powered, structure-preserving, 97.9% table accuracy) and traditional tools (PyMuPDF for speed, pdfplumber for quality). All processing is on-device with no API calls.
license	Apache 2.0 (see LICENSE.txt)

PDF Text Extraction

Tool Selection

Tool	Speed	Quality	Structure	Use When
Docling	0.43s/page	Good	✓ Yes	Need headers/tables/lists, academic PDFs, LLM consumption
PyMuPDF	0.01s/page	Excellent	✗ No	Speed critical, simple text extraction, good enough quality
pdfplumber	0.44s/page	Perfect	✗ No	Maximum fidelity needed, slow acceptable

Decision:

• Academic research → Docling (structure preservation)
• Batch processing → PyMuPDF (60x faster)
• Critical accuracy → pdfplumber (0 quality issues)

Installation

# Create virtual environment
python3 -m venv pdf_env
source pdf_env/bin/activate

# Install Docling (AI-powered, recommended)
pip install docling

# Install traditional tools
pip install pymupdf pdfplumber

First run downloads ML models (~500MB-1GB, cached locally, no API calls).

Basic Usage

Docling (Structure-Preserving)

from docling.document_converter import DocumentConverter

converter = DocumentConverter()  # Reuse for multiple PDFs
result = converter.convert("paper.pdf")
markdown = result.document.export_to_markdown()

# Save output
with open("paper.md", "w") as f:
    f.write(markdown)

Output includes: Headers (##), tables (|...|), lists (- ...), image markers.

PyMuPDF (Fast)

import fitz

doc = fitz.open("paper.pdf")
text = "\n".join(page.get_text() for page in doc)
doc.close()

with open("paper.txt", "w") as f:
    f.write(text)

pdfplumber (Highest Quality)

import pdfplumber

with pdfplumber.open("paper.pdf") as pdf:
    text = "\n".join(page.extract_text() or "" for page in pdf.pages)

with open("paper.txt", "w") as f:
    f.write(text)

Batch Processing

See examples/batch_convert.py for ready-to-use script.

Pattern:

from pathlib import Path
from docling.document_converter import DocumentConverter

converter = DocumentConverter()  # Initialize once
for pdf in Path("./pdfs").glob("*.pdf"):
    result = converter.convert(str(pdf))
    markdown = result.document.export_to_markdown()
    Path(f"./output/{pdf.stem}.md").write_text(markdown)

Performance tip: Reuse converter instance. Reinitializing wastes time.

Quality Considerations

Common issues:

• Ligatures: /uniFB03 → "ffi" (post-process with regex)
• Excessive whitespace: 50-90 instances (Docling has fewer)
• Hyphenation breaks: End-of-line hyphens may remain

Quality metrics script: See examples/quality_analysis.py

Benchmarks: See references/benchmarks.md for enterprise production data.

Troubleshooting

Slow first run: ML models downloading (15-30s). Subsequent runs fast.

Out of memory: Reduce concurrent conversions, process large PDFs individually.

Missing tables: Ensure do_table_structure=True in Docling options.

Garbled text: PDF encoding issue. Apply ligature fixes post-processing.

Privacy

All tools run on-device. No API calls, no data sent externally. Docling downloads models once, caches locally (~500MB-1GB).

References

• Tool comparison: references/tool-comparison.md
• Quality metrics: references/quality-metrics.md
• Production benchmarks: references/benchmarks.md