Comprehensive Benchmarking of Python Text Extraction Libraries in 2025: Insights and Recommendations
In the rapidly evolving landscape of document processing, choosing the right Python library for text extraction can significantly impact your project’s efficiency and reliability. Recently, I undertook an extensive benchmarking study of four prominent Python-based text extraction tools to shed light on their real-world performance. Here’s a detailed analysis that can guide your decisions in 2025.
Understanding the Scope of Testing
Libraries Evaluated:
– Kreuzberg (Self-developed, 71MB, 20 dependencies)
– Docling (IBM’s ML-powered solution, 1,032MB, 88 dependencies)
– MarkItDown (Microsoft’s Markdown converter, 251MB, 25 dependencies)
– Unstructured (Enterprise-focused processing, 146MB, 54 dependencies)
Test Methodology:
– Sample Size: 94 diverse documents, including PDFs, Word files, HTML pages, images, and spreadsheets
– Document Sizes: Ranging from small (<100KB) to massive (>50MB)
– Languages: English, Hebrew, German, Chinese, Japanese, Korean
– Processing Environment: CPU-only, no GPU acceleration
– Metrics Assessed: Speed, memory consumption, success/failure rates, installation footprint
Key Findings and Performance Insights
Speed and Efficiency:
– Kreuzberg emerged as the fastest, processing over 35 files per second, making it ideal for high-throughput environments.
– Unstructured offers solid reliability with decent speed, suitable for enterprise scenarios.
– MarkItDown performs well with straightforward documents but struggles with complex or large files.
– Docling’s processing time was often prohibitively long, exceeding 60 minutes per document, limiting its practicality for routine tasks.
Installation and Resource Footprint:
– Kreuzberg boasts a lightweight setup at approximately 71MB with minimal dependencies.
– Unstructured is larger but still manageable at 146MB.
– MarkItDown, at around 251MB, includes dependencies like ONNX, which may be unnecessary for simpler use cases.
– Docling is the heaviest, with a 1GB+ install size, driven by its comprehensive ML models and dependencies.
Reliability and Compatibility:
– Kreuzberg demonstrated consistent performance across all document types and sizes.
– Unstructured proved to be the most reliable, successfully processing over 88% of documents without failure.
– Both MarkItDown and Docling encountered challenges with large and complex files, often failing or timing out.
Choosing the Right Tool Based on
