Complex tables often lose their meaning when flattened into text. Learn how to preserve structure and context so your AI systems can actually use the data inside them.
Speakers
Overview:
Extracting structured insights from documents is rarely as simple as pulling plain text. Tables often contain dense relationships, merged cells, multi-row headers, and embedded context that traditional OCR or parsing pipelines flatten and lose. For organizations that depend on data accuracy — whether for financial reports, contracts, or regulatory filings — mishandling tables can lead to costly errors and missed insights.
In this webinar, we’ll show how Unstructured’s AI-driven approach makes it possible to preserve the semantics of complex tables, not just the raw text. You’ll learn how our framework handles layout, hierarchy, and relationships between table elements so downstream models and workflows can reason over them with confidence.
Technical Details:
In this session, we’ll walk through:
- How Unstructured represents table structure to maintain row/column relationships and context.
- Using evals to measure and improve table extraction accuracy.
- Real-world examples: financial statements, forms, and regulatory filings where table fidelity is critical.
- Best practices for integrating table-aware extraction into your existing AI pipelines.
This webinar includes a live demo and an open Q&A where you can get your questions answered. Can't join live? Register anyway to receive the recording!