Comparing Four Python Text Extraction Libraries: Benchmark Results for 2025

2025 Benchmark Recap: Evaluating Top Python Text Extraction Libraries for Developers and Data Scientists

Are you overwhelmed by the multitude of Python libraries available for extracting text from documents? Wondering which tool balances speed, reliability, and ease of integration? In this comprehensive analysis, we put four leading text extraction libraries through rigorous testing across a diverse set of real-world documents to help you make informed decisions.

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Explore the detailed benchmarks and visualizations here: Interactive Benchmark Dashboard

Background and Purpose

As the creator of Kreuzberg, a Python library dedicated to efficient text extraction, I was motivated to conduct a transparent and in-depth performance evaluation of several popular solutions. This was not about promotional flair but about gathering objective data on how these tools perform in practical scenarios involving varying document types and sizes.

The benchmarking process is fully automated, reproducible, and openly accessible, allowing developers and organizations to assess the most suitable library for their use cases.

Libraries Tested

Kreuzberg: My own lightweight library designed for speed and versatility.
Docling: IBM’s advanced machine learning-based extractor, known for its enterprise capabilities.
MarkItDown: Microsoft’s Markdown conversion tool, often used for simple document parsing.
Unstructured: An enterprise-grade framework supporting complex document workflows.

Testing Methodology

Document Types: PDFs, Word files, HTML pages, images, and spreadsheets.
Dataset: 94 documents ranging from tiny files (<100KB) to massive academic papers (>50MB).
Languages: English, Hebrew, German, Chinese, Japanese, Korean.
Environment: All tests executed on CPU-only setups to ensure fairness.
Metrics: Processing speed, memory consumption, success/failure rates, and installation size.
Repetition: Each document analyzed three times for statistical reliability.

Key Findings and Insights

Performance Highlights

Fastest and Most Reliable: Kreuzberg leads with processing speeds exceeding 35 documents per second, demonstrating robustness across all document categories.
Competent for Simpler Tasks: MarkItDown excels with basic PDF and Office documents but falters as complexity grows.
Enterprise Solutions: Unstructured offers consistent reliability with an impressive success rate above 88%, accommodating diverse document formats.
Resource Heavy & Slow: Docling’s ML-driven approach