Comprehensive Benchmarking of Python Text Extraction Libraries in 2025: Insights and Performance Analysis
Understanding the performance differences among Python-based text extraction tools can significantly impact your project’s efficiency and reliability. In 2025, I undertook an extensive evaluation of four prominent libraries to guide developers and data scientists in choosing the right solution for their needs. This in-depth review covers speed, resource consumption, robustness, and suitability across diverse document types and sizes.
Explore the Results Live
Visit the interactive dashboard to see up-to-date results: Benchmark Dashboard
Introduction: Why Benchmark?
While developing Kreuzberg—an efficient Python library for document processing—I recognized the necessity of understanding how different tools perform under real-world conditions. This motivation led me to design a comprehensive, unbiased benchmarking framework involving 94 authentic documents, totaling approximately 210MB, ranging from small text snippets to large academic articles.
The goal was transparency: providing users with concrete data rather than marketing claims. The benchmarks are fully automated, reproducible, and open-source, ensuring fair comparisons based on rigorous methodology.
Libraries Under Evaluation
The benchmarking process scrutinized four leading Python libraries:
- Kreuzberg (approx. 71MB, 20 dependencies) — my creation, emphasizing speed and lightweight deployment.
- Docling (around 1GB, 88 dependencies) — IBM’s machine learning-powered document understanding tool.
- MarkItDown (about 251MB, 25 dependencies) — Microsoft’s Markdown conversion utility.
- Unstructured (roughly 146MB, 54 dependencies) — An enterprise-focused document processing framework.
Testing Scope and Methodology
The evaluation encompassed:
- A diverse set of 94 documents: PDFs, Word files, HTML pages, images, and spreadsheets.
- Multiple size categories: from tiny archives (<100KB) up to very large files (>50MB).
- Multilingual content: including English, Hebrew, German, Chinese, Japanese, and Korean.
- CPU-only processing mode to ensure fair comparison across resources.
- Multiple performance metrics: processing speed, memory footprint, success rates, and installation size.
- Repeated runs for statistical robustness, with automated timeout handling (5-minute cap per task).
Key Results: What Did We Find?
Speed and Efficiency
- Kreuzberg demonstrated remarkable throughput, capable of processing over 35 files per second across various document types.
- **Unstructured
