# RAG OCR and Layout-Aware Document Parsing
Status: public
Confidence: medium (0.725) (verified)
Last verified: 2026-06-02
Generation: ai_structured


## TL;DR

OCR and layout parsing help RAG systems preserve tables, page structure, and citation anchors from documents that are not clean plain text.

## Core Explanation

Many enterprise documents are PDFs, scans, forms, slides, and tables. If ingestion flattens these into plain paragraphs, retrieval can lose cell relationships, headings, page numbers, and visual order. Layout-aware parsing gives the retriever richer evidence for citations and answer grounding.

Agents should record parser version, page IDs, coordinates, OCR confidence, table structure, and extraction warnings. This makes it easier to explain why a retrieved chunk came from a particular page region.

## Source-Mapped Facts

- Azure AI Document Intelligence documentation describes the Layout model as extracting text, tables, selection marks, and structure from documents. ([source](https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/prebuilt/layout?view=doc-intel-4.0.0))
- Amazon Textract documentation describes DetectDocumentText as detecting lines of text and words in a document. ([source](https://docs.aws.amazon.com/textract/latest/dg/how-it-works-detecting.html))
- Google Document AI documentation describes processors that can extract, classify, split, and parse documents. ([source](https://cloud.google.com/document-ai/docs/overview))

## Further Reading

- [Azure Document Intelligence Layout Model](https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/prebuilt/layout?view=doc-intel-4.0.0)
- [Amazon Textract Detecting Text](https://docs.aws.amazon.com/textract/latest/dg/how-it-works-detecting.html)
- [Google Document AI Overview](https://cloud.google.com/document-ai/docs/overview)