Comprehensive Benchmarking of Python Text Extraction Libraries in 2025: Insights for Developers and Data Professionals
In the rapidly evolving landscape of document processing, choosing the right Python library for text extraction can significantly impact your project’s performance and reliability. As part of ongoing efforts to provide transparent and data-driven recommendations, I’ve conducted an extensive performance evaluation of four prominent Python text extraction libraries using a diverse set of real-world documents. Here’s what you need to know.
Understanding the Context
The goal was to create an objective, reproducible benchmark that evaluates how these libraries perform across various document types, sizes, and languages. This initiative was motivated by my involvement with Kreuzberg, a Python library tailored for OCR and document parsing. However, the benchmarking process is fully independent, transparent, and open-source, designed to serve the broader community.
Libraries Under Review
The analysis included:
– Kreuzberg: My lightweight, fast library (~71MB, 20 dependencies), optimized for production environments.
– Docling: An enterprise-grade solution (~1GB, 88 dependencies) leveraging Machine Learning for complex document understanding.
– MarkItDown: A Markdown-focused converter (~251MB, 25 dependencies) suited for basic documents.
– Unstructured: A flexible, enterprise-oriented framework (~146MB, 54 dependencies) capable of handling diverse formats.
Benchmark Scope and Methodology
The evaluation encompassed 94 authentic documents, ranging from simple text files to large academic papers (up to 59MB). The datasets included PDFs, Word documents, HTML pages, images, and spreadsheets, covering six languages such as English, Hebrew, and Chinese. All tests were conducted on CPU-only systems to ensure fair comparisons, measuring metrics like processing speed, memory consumption, success rate, and installation size.
Key Findings and Performance Highlights
Speed and Efficiency
– Kreuzberg emerged as the fastest, processing over 35 files per second and reliably handling all document types.
– Unstructured demonstrated moderate speed with high reliability, making it suitable for complex tasks.
– MarkItDown performed well on straightforward documents but struggled with larger or complex files.
– Docling was notably slow, often taking over an hour per file, with frequent timeouts on medium-sized documents.
Installation Footprint
– Kreuzberg boasts the smallest installation size (~71MB) with minimal dependencies.
– Unstructured requires around 146MB.
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