Comprehensive Benchmarking of Python Text Extraction Libraries in 2025: Insights and Recommendations
In the rapidly evolving landscape of document processing, selecting the right Python library for text extraction can be a daunting task. To aid developers and data scientists, a recent extensive benchmarking study evaluated four prominent libraries using a diverse set of real-world documents. Here’s a detailed overview of the findings, practical insights, and guidance on choosing the optimal tool for your needs.
Overview of Benchmarking Objectives
The primary goal was to objectively compare the performance, reliability, and resource consumption of leading Python text extraction libraries. The study involved testing four solutions against 94 real documents—including assorted formats like PDFs, Word files, HTML, images, and spreadsheets—spanning sizes from tiny files (<100KB) to massive academic papers (>50MB) in multiple languages.
All tests were conducted in a controlled, CPU-only environment, ensuring consistent conditions across libraries. The comprehensive data set and transparent methodology are openly accessible, encouraging replication and ongoing evaluation.
The Libraries Under Evaluation
- Kreuzberg (Self-developed; 71MB, 20 dependencies): Known for its speed and lightweight footprint.
- Docling (IBM’s ML-powered solution; 1,032MB, 88 dependencies): Leveraging machine learning for deep document understanding.
- MarkItDown (Microsoft; 251MB, 25 dependencies): Focused on Markdown conversion and simple document parsing.
- Unstructured (Enterprise-grade; 146MB, 54 dependencies): Designed for large-scale, complex document processing.
Key Performance Highlights
Processing Speed
- Kreuzberg emerged as the clear leader in raw throughput, capable of processing over 35 documents per second, demonstrating exceptional efficiency across diverse formats.
- Unstructured delivered solid, reliable performance with moderate speeds suitable for enterprise workflows.
- MarkItDown performed admirably with straightforward documents but showed limitations with complex files.
- Docling often lagged significantly, sometimes taking over an hour per file due to heavy ML computations and dependency overhead.
Installation and Resource Usage
- Kreuzberg maintained the smallest footprint, at just 71MB, with minimal dependencies, making it ideal for deployment in constrained environments like AWS Lambda or edge devices.
- Unstructured was larger but still manageable, with a moderate number of dependencies.
- MarkItDown was heavier due to framework overhead but still
