Comprehensive Benchmarking of Python Text Extraction Libraries: 2025 Performance Insights

Introduction: Evaluating Leading Tools for Robust and Efficient Text Extraction

In the rapidly evolving world of document processing, selecting the right Python library for text extraction is crucial. To assist developers and data scientists in making informed decisions, I undertook an extensive benchmarking study of four prominent Python text extraction libraries, analyzing their performance across a diverse set of real-world documents. This in-depth review aims to provide clarity on speed, reliability, installation footprint, and suitability for various use cases.

Benchmarking Methodology and Data Set

To ensure fairness and relevance, the benchmarks were conducted using 94 authentic documents, encompassing formats such as PDFs, Word files, HTML pages, images, and spreadsheets. These ranged from small snippets under 100KB to large academic papers exceeding 50MB, and included multiple languages including English, Hebrew, German, Chinese, Japanese, and Korean. The environment was configured to process data with CPU-only resources, avoiding GPU acceleration to reflect typical deployment scenarios.

The libraries evaluated include:

• Kreuzberg: A lightweight, efficient, and versatile solution designed for production environments.
• Unstructured: Known for enterprise-grade reliability and broad document support.
• MarkItDown: Microsoft’s markdown-centric parser optimized for simple documents.
• Docling: An ML-powered, research-focused library that excels with complex, structured data.

Each library was tested multiple times per document, with metrics collected on processing speed, memory usage, success rates, and installation sizes.

Key Findings and Performance Highlights

Speed and Efficiency

• Kreuzberg emerged as the fastest, processing over 35 documents per second, demonstrating outstanding performance across all document types and sizes.
• Unstructured provided a reliable middle ground, balancing speed with high success rates.
• MarkItDown handled straightforward documents efficiently but struggled with complex or large files.
• Docling’s processing times ballooned significantly, often taking over an hour per document, highlighting its intensive resource demands.

Installation Footprint and Resource Usage

• Kreuzberg maintains a minimal installation size (~71MB) with only 20 dependencies, favoring quick setup and deployment.
• Unstructured is larger (~146MB) with more dependencies, suited for scenarios where reliability outweighs minimalism.
• MarkItDown, at approximately 251MB, includes advanced features like ONNX integration, but comes with increased overhead.
• Docling’s hefty footprint exceeds 1GB, along with 88 dependencies, making it less ideal for resource-constrained