Comparative Analysis of Python Text Extraction Libraries: Benchmark Results for 2025

In the rapidly evolving landscape of document processing, selecting the right text extraction library can significantly impact your project’s efficiency and reliability. To aid developers and data scientists in making informed decisions, I conducted a comprehensive benchmarking study of four popular Python libraries, analyzing their performance across a diverse set of real-world documents.

Benchmark Overview

The analysis evaluates Kreuzberg, Docling, MarkItDown, and Unstructured using a curated collection of 94 documents that span various formats, sizes, and languages. These include PDFs, Word documents, HTML pages, images, and spreadsheets, with file sizes ranging from tiny (<100 KB) to massive (>50 MB). The benchmarks focus on multiple metrics such as processing speed, memory consumption, success rates, and installation footprint, all conducted in a CPU-only environment for consistency.

Key Findings

• Processing Speed:
  Kreuzberg emerged as the fastest, capable of processing over 35 files per second across all document types. Unstructured demonstrated commendable reliability with moderate speed, while MarkItDown performed well on straightforward files but struggled with complexity. Docling, despite its advanced ML capabilities, often took over an hour per document and frequently timed out on medium-sized files.

• Installation Footprint:
  The libraries varied substantially in size. Kreuzberg’s minimal footprint at approximately 71MB with only 20 dependencies makes it highly suitable for production environments and serverless deployments. In contrast, Docling’s installation size exceeds 1GB due to its extensive dependencies, posing challenges for lightweight deployment.

• Reliability and Success Rates:
  Unstructured registered the highest success rate, overcoming complex layouts and multilingual content effectively (success rate over 88%). Kreuzberg showcased consistent performance across diverse document types. MarkItDown was efficient with simple, markdown-friendly documents but faltered with large or intricate files. Docling’s ML-based approach, while powerful in theory, often resulted in failures or excessive processing times.

Practical Recommendations

• For High-Speed, Low-Resource Environments:
  Kreuzberg stands out as the optimal choice, offering a blend of speed, lightweight architecture, and broad compatibility. Its support for synchronous/asynchronous processing and integrated OCR capabilities add to its versatility.

• For Enterprise-Grade Reliability:
  The Unstructured library excels in handling complex, multilingual, and varied document formats, making it suitable for enterprise applications where stability is paramount.