Comprehensive Benchmarking of Python Text Extraction Libraries in 2025: What You Need to Know
In the rapidly evolving realm of document processing, choosing the right tool for text extraction can significantly impact your project’s performance, reliability, and deployment footprint. As part of my ongoing work with Kreuzberg—a Python library designed for efficient text extraction—I recently undertook an extensive benchmarking study of four popular libraries. Here’s a detailed analysis of the findings to help you make informed decisions in 2025.
Exploring the Benchmark
Objective and Methodology
My goal was to provide an honest, data-driven comparison of leading Python text extraction libraries. This assessment covers 94 real-world documents, from simple text files to large academic papers, totaling approximately 210MB. The evaluation doesn’t rely on synthetic tests; instead, it tests typical use cases across various formats and languages, including English, Hebrew, German, Chinese, Japanese, and Korean.
The libraries benchmarked include:
- Kreuzberg (my creation) – a lightweight, fast, and versatile solution
- Docling – an enterprise-grade ML-powered library
- MarkItDown – a Markdown-centric document converter
- Unstructured – a comprehensive tool tailored for complex enterprise documents
Evaluation criteria encompassed speed, resource consumption, success rates, installation size, and robustness across different document types and sizes.
Key Results and Insights
Performance Highlights
- Fastest Processor: Kreuzberg stands out with an impressive rate of over 35 documents per second, maintaining consistent reliability across diverse formats and sizes.
- Reliability Champion: Unstructured demonstrates exceptional stability, successfully processing over 88% of documents, including highly complex layouts.
- Middle of the Pack: MarkItDown performs well with straightforward files but falters with larger or more intricate documents.
- Heavyweight and Slow: Docling can take upwards of an hour to process a medium-sized file, with frequent timeouts and failures, especially on larger datasets.
Installation and Footprint
- Kreuzberg shines with a minimal footprint of just 71MB and only 20 dependencies, making it ideal for deployment in resource-constrained environments.
- Unstructured requires 146MB with 54 dependencies, suitable for enterprise settings where reliability is paramount.
- MarkItDown’s size of 251MB (including ONNX dependencies) offers moderate footprint for its capabilities.
- Conversely, Docling’s hefty 1,032MB size poses challenges for practical deployment.
Practical Recommendations
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