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View Full Features →The open-source library handles most document types but uses simpler extraction models. The API uses more sophisticated table extraction (vision models), better OCR, and higher-quality element classification. For production RAG systems with complex documents, the API produces noticeably better results.
Yes, through integrated OCR. The open-source version uses Tesseract, and the API uses more advanced OCR models. Quality depends on scan resolution — clean scans at 300+ DPI produce good results. Low-quality scans, handwriting, or unusual fonts degrade accuracy.
Unstructured handles a wider range of document formats (not just PDFs) and provides more deployment flexibility (local, API, enterprise). LlamaParse often produces better results for complex PDFs with tables and figures because it uses LLM-powered extraction. For PDF-heavy workloads, test both; for multi-format document ETL, Unstructured is more comprehensive.
The open-source library processes roughly 1-5 pages per second depending on complexity and whether OCR is needed. The API is faster with parallelization. For large collections (10K+ documents), use the Platform product or batch API with concurrent requests.
It preserves structural elements (headers become Title elements, lists become ListItem elements) but not inline formatting like bold or italic. The output is semantic elements with types, not formatted text. This is by design — the element classification is more useful for RAG than formatting preservation.
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