Comprehensive Benchmarking of Python Text Extraction Libraries in 2025: Insights for Developers
In the rapidly evolving realm of document processing, choosing the right text extraction tool can significantly influence your project’s efficiency and reliability. To guide developers and data scientists alike, I’ve conducted an extensive and impartial performance evaluation of four prominent Python libraries for document text extraction. This benchmark spans a broad spectrum of real-world documents, providing clarity amid the often conflicting claims.
Why Benchmarking Matters
With numerous options available—each boasting unique features—determining the best fit for specific needs can be daunting. My goal was to eliminate guesswork by providing concrete data, covering aspects like speed, accuracy, resource consumption, and robustness across various document formats and sizes.
The Contenders
Here’s a snapshot of the libraries evaluated:
- Kreuzberg: My own lightweight, high-speed text extraction library designed for production environments.
- Docling: An IBM-backed Machine Learning solution, known for advanced document understanding capabilities.
- MarkItDown: A Microsoft project optimized for Markdown and straightforward document conversions.
- Unstructured: An enterprise-focused framework capable of handling diverse document types with reliability.
Test Campaign Overview
- Documents Analyzed: 94 real-world files—including PDFs, Word documents, web pages, images, and spreadsheets—in six languages (English, Hebrew, German, Chinese, Japanese, Korean).
- Size Range: From tiny sub-100KB files to large academic PDFs exceeding 50MB.
- Methodology: Automated benchmarking with 3 iterations per file, measuring processing speed, memory footprint, success rate, and installation size—executed on CPU-only setups to ensure fairness.
- Transparency: All code, test documents, and results are openly accessible for reproduction and scrutiny.
Key Findings
Performance and Speed
- Kreuzberg stands out with an impressive rate of processing over 35 files per second, demonstrating both speed and versatility.
- Unstructured offers satisfactory speed with high reliability, making it suitable for enterprise-grade pipelines.
- MarkItDown performs well with simple, straightforward documents but struggles with complex or large files.
- Docling exhibits significant latency—sometimes taking over an hour per document—and faces frequent timeouts on medium to large files.
Resource Consumption & Installation Footprint
- Kreuzberg: Light at just 71MB with only 20 dependencies
