Comprehensive Benchmarking of Python Text Extraction Libraries in 2025: Insights and Performance Analysis
In-Depth Performance Evaluation of Leading Python Text Extraction Tools
Staying ahead in text processing often requires choosing the right library for your project. To assist developers and researchers in making informed decisions, we conducted a thorough performance benchmarking of four prominent Python text extraction libraries. This extensive evaluation spans a diverse set of 94 real-world documents, revealing surprising insights into speed, reliability, and resource consumption.
Explore the live results here: Interactive Benchmark Dashboard
Setting the Stage: Why Benchmark?
As the creator of Kreuzberg, a Python-based text extraction library, I aimed to establish an impartial, comprehensive performance comparison. The goal was to evaluate real-world performance across various document types, sizes, and languages—without marketing spin or cherry-picking results. This benchmark is fully automated, reproducible, and based on an open-source methodology, ensuring transparency and repeatability.
The Libraries Under Test
Our comparison included four distinct libraries, each with unique strengths:
- Kreuzberg: A lightweight, high-speed solution optimized for production environments. (71MB, 20 dependencies)
- Docling: An advanced enterprise-grade tool powered by machine learning, capable of complex document understanding. (1GB+, 88 dependencies)
- MarkItDown: A Microsoft-developed Markdown conversion library suitable for simple document formats. (251MB, 25 dependencies)
- Unstructured: An enterprise-focused library adept at handling a wide range of document formats with high reliability. (146MB, 54 dependencies)
Testing Scope and Methodology
Our evaluation covered:
- 94 documents: Including PDFs, Word documents, HTML pages, images, and spreadsheets.
- Size spectrum: From small files (<100KB) to massive files (>50MB).
- Languages: Content in English, Hebrew, German, Chinese, Japanese, and Korean.
- Processing environment: CPU-only, no GPU acceleration, to ensure fair comparisons.
- Metrics: Processing speed, memory footprint, success rate, and package sizes.
Each test was repeated three times to ensure statistical robustness, with resource consumption monitored throughout. Timeouts were capped at five minutes per document to prevent disqualifications.
