Comprehensive Benchmarking of Python Text Extraction Libraries in 2025: Who Comes Out on Top?
In today’s data-driven landscape, extracting text from diverse document formats is crucial for automating workflows, data analysis, and AI applications. But with numerous Python libraries available, how do you choose the right tool for your needs? To answer this question, I conducted an extensive, impartial performance comparison of four popular text extraction libraries—streamlined, reliable, and built for different use cases.
Why This Benchmark Matters
As the creator of Kreuzberg, a lightweight and fast text extraction library, I wanted to evaluate its performance against existing solutions across a broad spectrum of real-world documents. This benchmark is fully transparent, reproducible, and designed to provide clear insights into each library’s strengths and limitations. Whether you’re working in production environments or research, understanding these differences can significantly influence your choice.
The Libraries Under Examination
Here’s a brief overview of the solutions tested:
- Kreuzberg: Our own optimized, minimal-footprint library (71MB, 20 dependencies). Known for speed and efficiency—ideal for production workloads, edge computing, and serverless setups.
- Docling: IBM’s AI-powered document understanding toolkit (1,032MB, 88 dependencies). Leverages Deep Learning, suitable for complex research tasks but resource-intensive.
- MarkItDown: Microsoft’s Markdown converter (251MB, 25 dependencies). Excels in simple document processing and content formatting.
- Unstructured: An enterprise-level document processing framework (146MB, 54 dependencies). Balances reliability and flexibility, suitable for varied business needs.
Scope and Methodology
We tested these libraries across 94 authentic documents—including PDFs, Word files, HTML pages, images, and spreadsheets—covering six languages: English, Hebrew, German, Chinese, Japanese, and Korean. The dataset spanned a size range from tiny (.1MB) to enormous (>50MB), ensuring comprehensive coverage of typical use cases.
All tests ran on a CPU-only environment to maintain fairness, employing automated benchmarking with multiple iterations, detailed resource monitoring, and failure logging. Results are publicly available in an interactive dashboard for deeper exploration.
Key Takeaways and Performance Highlights
Speed and Efficiency
- Kreuzberg consistently processes over 35 files per second, demonstrating exceptional speed across document types and sizes.
- Unstructured offers reliable performance but at a moderate pace.
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