Update articles/ai-services/content-understanding/choosing_guide_doc_processing.md

Co-authored-by: Patrick Farley <[email protected]>

Author: Additi

articles/ai-services/content-understanding/choosing_guide_doc_processing.md

@@ -17,7 +17,7 @@ ms.topic: overview
 As organizations increasingly rely on Generative AI to manage documents and unstructured data, selecting the right tools is essential for building robust, secure, and scalable document processing workflows. Let's review a comparative overview of the leading Azure AI solutions for intelligent document processing (IDP), helping you evaluate and choose the most effective approach for your business requirements. 
 
 ## Azure AI Document Intelligence 
-Azure AI Document Intelligence remains the trusted choice for many document-centric scenarios. Customers continue to rely on the industry leading OCR capability and structure extraction, including table recognition, figures, paragraphs, selection marks, sections, and more output in markdown format for easy integrations with LLMs for ingestion for RAG, field extraction and document chat scenarios. Document Intelligence has the tools to build scalable and flexible IDP solutions with classification and conditional routing for high-accuracy extraction from prebuilt models like invoices, receipts, tax forms, and identification cards. For any custom template, you can label a few samples to train a custom extraction model on any document type. Document Intelligence models have some limitation like supporting only extracting results, limited generalization of custom models across many template variations and limited semantic understanding capabilities. With confidence scores and grounded results, you can build an effective, low latency, consistent extractive document processing solution for most scenarios. Document Intelligence provides the following models:
+Azure AI Document Intelligence is the trusted choice for many document-centric scenarios. Customers rely on its industry leading OCR capability and structure extraction, including table recognition, figures, paragraphs, selection marks, sections, and more output in markdown format for easy integrations with LLMs for ingestion in RAG, field extraction, and document chat scenarios. Document Intelligence has the tools to build scalable and flexible IDP solutions with classification and conditional routing for high-accuracy extraction from prebuilt models like invoices, receipts, tax forms, and identification cards. For any custom template, you can label a few samples to train a custom extraction model on any document type. Document Intelligence models have some limitations like supporting only extracting results, limited generalization of custom models across many template variations, and limited semantic understanding capabilities. With confidence scores and grounded results, you can build an effective, low latency, consistent extractive document processing solution for most scenarios. Document Intelligence provides the following models:
 
 * Document digitization or [Optical Character Recognition (OCR)](/azure/ai-services/document-intelligence/prebuilt/read?view=doc-intel-4.0.0&branch=main&tabs=sample-code) to extract printed or handwritten text from documents.