Comprehensive 2025 Benchmarking of Python Text Extraction Libraries: What You Need to Know
Are you looking to integrate text extraction capabilities into your Python projects? With numerous libraries available, choosing the right one can be daunting. To help, I conducted an in-depth, unbiased performance analysis of four leading Python text extraction solutions using real-world documents. Here’s a summary of my findings and insights to guide your decision-making process.
Explore the live results here: Interactive Benchmark Dashboard
Understanding the Benchmark
The goal was to evaluate the performance, reliability, and resource consumption of four prominent Python libraries for text extraction:
- Kreuzberg: An open-source library developed by myself, optimized for speed and efficiency.
- Docling: IBM’s machine learning-powered solution supporting complex document understanding.
- MarkItDown: Microsoft’s Markdown processor suitable for straightforward conversion tasks.
- Unstructured: A versatile enterprise solution capable of handling diverse document formats.
Test Parameters included:
– 94 diverse, real-world documents: PDFs, Word documents, HTML, images, and spreadsheets.
– Size variation: from tiny files (<100KB) to massive datasets (>50MB).
– Multiple languages: English, Hebrew, German, Chinese, Japanese, Korean.
– Processing environment: CPU-only, no GPU acceleration to ensure fair comparison.
– Metrics: Speed, memory footprint, success rate, and installation size.
Performance Highlights
Speed & Efficiency:
– Kreuzberg emerged as the clear leader, processing over 35 files per second across various formats.
– Unstructured delivered solid consistency, excelling in handling complex layouts.
– MarkItDown performed at a decent clip for simple documents but struggled with complexity.
– Docling lagged significantly, often taking over an hour per file, with frequent timeouts on medium-sized documents.
Installation Footprint:
– Kreuzberg is remarkably lightweight at 71MB with just 20 dependencies.
– Unstructured follows with 146MB and slightly more dependencies.
– MarkItDown is larger, at 251MB, due to inclusion of Deep Learning components.
– Docling’s heavyweight at over 1GB and 88 dependencies makes it less suitable for resource-constrained environments.
Reliability & Practicality:
– Kreuzberg demonstrated consistent performance across all document types and sizes.
– Unstructured proved the most reliable with over 88% success rate in varied scenarios.
– MarkItDown is ideal for straightforward conversions but falters with
