I benchmarked 4 Python text extraction libraries so you don’t have to (2025 results)

Comprehensive Evaluation of Python Text Extraction Libraries in 2025: Benchmarks and Insights

In the rapidly evolving landscape of document processing, selecting the right text extraction tool can significantly influence your projects’ efficiency and reliability. To assist developers and stakeholders in making informed decisions, recent benchmarking data offers valuable clarity. This detailed analysis compares four prominent Python libraries across a diverse set of real-world documents, revealing the strengths and limitations of each.

Overview of the Benchmarking Initiative

This extensive benchmarking effort aimed to evaluate the performance, resource consumption, and robustness of four leading Python text extraction solutions. The tests encompassed a broad spectrum of documents—ranging from small text files to large academic papers—covering multiple formats such as PDFs, Word documents, HTML files, images, and spreadsheets. The primary goal was to ensure objective, reproducible results without marketing bias.

Libraries Compared

Kreuzberg (Self-developed): Known for its lightweight design and versatility.
Docling: An advanced machine learning-powered solution from IBM, designed for complex document understanding.
MarkItDown: Microsoft’s lightweight Markdown converter optimized for simplicity.
Unstructured: An enterprise-grade framework prioritizing reliability across diverse formats.

Methodology Highlights

Document Set: 94 documents spanning various formats, sizes, and languages (including English, Hebrew, Chinese, Japanese, Korean, and German).
Metrics: Speed (files per second), memory overhead, success rate, installation size, and failure cases.
Environment: CPU-only processing to emulate resource-constrained environments; multiple runs for statistical stability.
Tools: Automated CI/CD pipelines facilitated consistent, fair comparisons.
Open Data: All source code, test documents, and detailed results are publicly accessible for transparency.

Key Findings and Performance Insights

Speed and Efficiency