Comprehensive Benchmarking of Python Text Extraction Libraries (2025 Results): Which One Performs Best?
In the rapidly evolving world of document processing, selecting the right Python library for text extraction can be a challenge. To provide clarity, I recently conducted an extensive, unbiased benchmark of leading text extraction solutions, analyzing their performance across a diverse set of real-world documents. Here’s a detailed overview of my findings, designed to help you choose the most suitable tool for your needs.
Understanding the Scope
This evaluation compares four prominent Python libraries:
- Kreuzberg: An in-house solution developed by me, optimized for speed and flexibility.
- Docling: IBM’s machine learning-powered document understanding library.
- MarkItDown: Microsoft’s lightweight Markdown conversion tool.
- Unstructured: An enterprise-grade library supporting complex document types.
The assessment covers a broad spectrum of document styles—PDFs, Word files, HTML pages, images, and spreadsheets—sourced from 94 authentic files ranging from tiny snippets under 100KB to massive academic papers exceeding 50MB. The testing environment was consistent, with all libraries processed on CPU-only setups for fairness.
Key Performance Findings
Speed and Reliability:
- Kreuzberg emerged as the fastest, capable of processing over 35 documents per second, making it ideal for production environments requiring high throughput.
- Unstructured offered robust reliability across diverse formats, maintaining an impressive success rate, particularly with complex layouts.
- MarkItDown excelled on simple documents but struggled with intricate or large files.
- Docling faced significant performance challenges, often taking over an hour per document, and frequently timed out on medium to large files.
Installation and Resource Footprint:
- Kreuzberg boasts a minimal installation size (~71MB) with just 20 dependencies, facilitating easy deployment.
- Unstructured consumes approximately 146MB, with more dependencies (54), offering a balanced trade-off.
- MarkItDown has a larger footprint (~251MB), including optional components like ONNX.
- Docling, due to its comprehensive ML models, requires over 1GB of storage and involves numerous dependencies, making it less suitable for lightweight setups.
Practical Insights
- For high-performance, scalable applications, Kreuzberg stands out due to its speed and small size.
- When reliability is paramount—especially with diverse or complex documents—Unstructured
