Comprehensive Benchmarking of Python Text Extraction Libraries in 2025: Which Framework Reigns Supreme?
In the ever-evolving landscape of document processing, selecting the right Python library for text extraction can significantly influence your project’s efficiency and reliability. Recognizing this, I undertook an extensive, unbiased benchmarking study of four prominent libraries—Kreuzberg, Docling, MarkItDown, and Unstructured—using a diverse set of real-world documents. Here’s what I uncovered.
An In-Depth Performance Evaluation
Libraries Assessed:
- Kreuzberg: My creation, optimized for speed and compactness.
- Docling: IBM’s advanced machine learning solution.
- MarkItDown: Microsoft’s lightweight Markdown parser.
- Unstructured: A robust tool designed for enterprise-grade document workflows.
Testing Parameters:
- Analyzed 94 authentic documents — including PDFs, Word files, HTML, images, and spreadsheets.
- Varied file sizes from tiny (under 100KB) to extremely large (over 50MB).
- Covered multiple languages: English, Hebrew, German, Chinese, Japanese, Korean.
- Conducted tests on CPU-only systems to ensure fair hardware comparisons.
- Measured key metrics: processing speed, memory consumption, success rates, installation size.
Key Insights from the Benchmark Results
Performance Highlights:
| Rank | Library | Processing Speed | Reliability | Key Strengths |
|———|—————-|———————————-|—————————|———————————————-|
| 1 | Kreuzberg | Over 35 files per second | Consistently high | Fast, lightweight, handles all formats |
| 2 | Unstructured | Moderate but dependable | Over 88% success rate | Excels with complex structures |
| 3 | MarkItDown | Excellent for simple docs | Moderate | Ideal for basic PDFs and Markdown workflows |
| 4 | Docling | Up to hours per file (!!) | Variable, often slow | ILivAdvanced ML, but resource-heavy |
Installation Footprint:
| Library | Size | Dependencies |
|—————-|——–|———————|
| Kreuzberg | ~71MB | 20 dependencies |
| Unstructured | ~146MB | 54 dependencies |
| MarkItDown
