When PyMuPDF Can’t See the Table: Parse PDFs for RAG with Azure Layout
English summary
This tutorial from the Enterprise Document Intelligence series shows how Azure Document Intelligence’s layout model extracts relational tables from PDFs where PyMuPDF falls short. The Azure approach preserves native table cells and works on scanned pages via integrated OCR. It also retrieves captions and headings without relying on regular expressions. The method is presented as a superior parsing step for Retrieval Augmented Generation (RAG) pipelines.
Chinese summary
这篇企业文档智能系列教程展示了Azure文档智能的布局模型如何在PyMuPDF未能识别表格时，从PDF中提取关系型表格。Azure方案保留了原生表格单元格，并通过集成OCR支持扫描页面及图像。它还能在不依赖正则表达式的情况下提取标题和标题。该方法被呈现为检索增强生成（RAG）流程中更优的解析步骤。
Key points
Azure Layout preserves relational table structure and native table cells, overcoming a key limitation of PyMuPDF.
Azure Layout保留了关系型表格结构和原生表格单元格，克服了PyMuPDF的一个主要局限。
Built-in OCR handles scanned pages and images, enabling table extraction from non-digital PDFs.
内置OCR可处理扫描页面和图像，使非数字PDF也能提取表格。
Captions and headings are extracted without regex, simplifying document parsing for RAG.
无需正则表达式即可提取标题和标题，简化了RAG的文档解析。
The tutorial provides a practical alternative for PDF parsing in retrieval-augmented generation pipelines.
本教程为检索增强生成流程中的PDF解析提供了一个实用的替代方案。