Question

A logistics company processes multi-page shipping manifests as scanned PDFs. Each page contains complex nested tables where cargo line items span multiple rows and are grouped under section headers. The data science team needs a clean, grounded representation that preserves the table structure (row/column relationships and section groupings) so a downstream RAG agent can reason accurately about which items belong to which shipment section. Which approach produces the most reliable structured representation for this requirement?

Accepted Answer

Use an Azure AI Content Understanding document analyzer configured to emit markdown output that preserves layout, table structure, and hierarchical headings for grounding.

Answer

Run the Read (OCR) model to extract raw text lines, then concatenate all detected text into a single plain-text blob for chunking.

Answer

Apply Azure AI Vision image captioning to each page to describe the tables in natural language before indexing.

Answer

Use key phrase extraction on the OCR output to identify the most important terms per page and index those as the grounded representation.