Comprehensive Benchmarking of Python Text Extraction Libraries in 2025: Performance, Reliability, and Insights

In the evolving landscape of document processing, selecting the right Python library for text extraction can significantly impact your project’s efficiency and accuracy. To assist developers and organizations in making informed decisions, we have conducted an extensive, unbiased comparison of four prominent text extraction tools, analyzing their performance across diverse real-world documents.

What Was Tested?

Our comparative analysis encompassed the following libraries:

• Kreuzberg: An open-source tool designed for fast and reliable text extraction, with a focus on minimal dependencies.
• Docling: An advanced machine learning-powered solution from IBM, optimized for complex and enterprise-grade document understanding.
• MarkItDown: A lightweight converter tailored for Markdown and simple document formats from Microsoft.
• Unstructured: An enterprise-focused library excelling in processing a wide range of document types with a focus on robustness.

The benchmarking involved processing 94 authentic documents—ranging from small text files and Office documents to large academic papers and multimedia-rich PDFs. File sizes spanned from under 100KB to over 50MB, including content in multiple languages such as English, Hebrew, German, Chinese, Japanese, and Korean.

Key Findings

Processing Speed

Speed variation was among the most striking results:

• Kreuzberg emerged as the frontrunner, capable of processing over 35 files per second, handling diverse formats seamlessly.
• Unstructured demonstrated solid reliability but traded-off some speed for robustness.
• MarkItDown performed efficiently with straightforward documents but struggled with complex or larger files.
• Docling often took over 60 minutes per document—a notable concern for high-volume scenarios.

Installation and Deployment Footprint

The size of the libraries can influence deployment decisions, especially for resource-constrained environments:

• Kreuzberg boasts a compact footprint at approximately 71MB with only 20 dependencies.
• Unstructured measures around 146MB with 54 dependencies.
• MarkItDown is larger at 251MB, partly due to inclusion of auxiliary tools like ONNX.
• Docling is the largest, exceeding 1GB and involving 88 dependencies, reflecting its comprehensive ML model integrations.

Reliability and Success Rates

• Unstructured achieved the highest success rate (>88%) across challenging files, indicating