Merge pull request #6105 from PatrickFarley/cu-updates

Cu updates

Author: v-regandowner

articles/ai-services/computer-vision/overview-ocr.md

@@ -17,29 +17,29 @@ ms.custom: devx-track-csharp
 # OCR - Optical Character Recognition
 
 > [!WARNING]
-> This service, including the Azure AI Vision legacy [OCR API in v3.2](/rest/api/computervision/recognize-printed-text?view=rest-computervision-v3.2) and [RecognizeText API in v2.1](/rest/api/computervision/recognize-printed-text/recognize-printed-text?view=rest-computervision-v2.1), is not recommended for use.
+> We don't recommend using this service, including the Azure AI Vision legacy [OCR API in v3.2](/rest/api/computervision/recognize-printed-text?view=rest-computervision-v3.2) and [RecognizeText API in v2.1](/rest/api/computervision/recognize-printed-text/recognize-printed-text?view=rest-computervision-v2.1).
 
 [!INCLUDE [read-editions](includes/read-editions.md)]
 
 
-OCR or Optical Character Recognition is also referred to as text recognition or text extraction. Machine-learning-based OCR techniques allow you to extract printed or handwritten text from images such as posters, street signs and product labels, as well as from documents like articles, reports, forms, and invoices. The text is typically extracted as words, text lines, and paragraphs or text blocks, enabling access to digital version of the scanned text. This eliminates or significantly reduces the need for manual data entry.
+OCR or Optical Character Recognition is also referred to as text recognition or text extraction. Machine-learning-based OCR techniques allow you to extract printed or handwritten text from images such as posters, street signs, and product labels, as well as from documents like articles, reports, forms, and invoices. The text is typically extracted as words, text lines, and paragraphs or text blocks, enabling access to digital version of the scanned text. This capability eliminates or significantly reduces the need for manual data entry.
 
 
 
 ## OCR engine
 
-Microsoft's **Read** OCR engine is composed of multiple advanced machine-learning based models supporting [global languages](./language-support.md). It can extract printed and handwritten text including mixed languages and writing styles. **Read** is available as cloud service and on-premises container for deployment flexibility. It's also available as a synchronous API for single, non-document, image-only scenarios with performance enhancements that make it easier to implement OCR-assisted user experiences.
+Microsoft's **Read** OCR engine uses multiple advanced machine-learning models that support [global languages](./language-support.md). It extracts printed and handwritten text, including mixed languages and writing styles. You can use **Read** as a cloud service or as an on-premises container for flexible deployment. It's also available as a synchronous API for single, non-document, image-only scenarios with performance enhancements that simplify implementing OCR-assisted user experiences.
 
 
 
 
 ## How is OCR related to Intelligent Document Processing (IDP)?
 
-Intelligent Document Processing (IDP) uses OCR as its foundational technology to additionally extract structure, relationships, key-values, entities, and other document-centric insights with an advanced machine-learning based AI service like [Document Intelligence](../../ai-services/document-intelligence/overview.md). Document Intelligence includes a document-optimized version of **Read** as its OCR engine while delegating to other models for higher-end insights. If you are extracting text from scanned and digital documents, use [Document Intelligence Read OCR](../document-intelligence/prebuilt/read.md).
+Intelligent Document Processing (IDP) uses OCR as its foundational technology to extract structure, relationships, key-values, entities, and other document-centric insights with an advanced machine-learning based AI service like [Document Intelligence](../../ai-services/document-intelligence/overview.md). Document Intelligence includes a document-optimized version of **Read** as its OCR engine while delegating to other models for higher-end insights. If you're extracting text from scanned and digital documents, use [Document Intelligence Read OCR](../document-intelligence/prebuilt/read.md).
 
 ## How to use OCR
 
-Try out OCR by using Vision Studio. Then follow one of the links to the Read edition that best meet your requirements.
+Try out OCR by using Vision Studio. Then follow one of the links to the Read edition that best meets your requirements.
 
 > [!div class="nextstepaction"]
 > [Try Vision Studio](https://portal.vision.cognitive.azure.com/)
@@ -48,7 +48,7 @@ Try out OCR by using Vision Studio. Then follow one of the links to the Read edi
 
 ## OCR supported languages
 
-Both **Read** versions available today in Azure AI Vision support several languages for printed and handwritten text. OCR for printed text includes support for English, French, German, Italian, Portuguese, Spanish, Chinese, Japanese, Korean, Russian, Arabic, Hindi, and other international languages that use Latin, Cyrillic, Arabic, and Devanagari scripts. OCR for handwritten text includes support for English, Chinese Simplified, French, German, Italian, Japanese, Korean, Portuguese, and Spanish languages.
+Both **Read** versions available today in Azure AI Vision support several languages for printed and handwritten text. OCR for printed text supports English, French, German, Italian, Portuguese, Spanish, Chinese, Japanese, Korean, Russian, Arabic, Hindi, and other international languages that use Latin, Cyrillic, Arabic, and Devanagari scripts. OCR for handwritten text supports English, Chinese Simplified, French, German, Italian, Japanese, Korean, Portuguese, and Spanish languages.
 
 Refer to the full list of [OCR-supported languages](./language-support.md#optical-character-recognition-ocr).
 
@@ -57,24 +57,24 @@ Refer to the full list of [OCR-supported languages](./language-support.md#optica
 The Read OCR model is available in Azure AI Vision and Document Intelligence with common baseline capabilities while optimizing for respective scenarios. The following list summarizes the common features:
 
 * Printed and handwritten text extraction in supported languages
-* Pages, text lines and words with location and confidence scores
+* Pages, text lines, and words with location and confidence scores
 * Support for mixed languages, mixed mode (print and handwritten)
 * Available as Distroless Docker container for on-premises deployment
 
 ## Use the OCR cloud APIs or deploy on-premises
 
-The cloud APIs are the preferred option for most customers because of their ease of integration and fast productivity out of the box. Azure and the Azure AI Vision service handle scale, performance, data security, and compliance needs while you focus on meeting your customers' needs.
+Most customers prefer the cloud APIs because they're easy to integrate and offer fast productivity out of the box. Azure and the Azure AI Vision service handle scale, performance, data security, and compliance needs while you focus on meeting your customers' needs.
 
 For on-premises deployment, the [Read Docker container](./computer-vision-how-to-install-containers.md) enables you to deploy the Azure AI Vision v3.2 generally available OCR capabilities in your own local environment. Containers are great for specific security and data governance requirements.
 
 
 ## Input requirements
 
-The **Read** API takes images and documents as its input. The images and documents must meet the following requirements:
+The **Read** API takes images and documents as input. The images and documents must meet the following requirements:
 
 * Supported file formats are JPEG, PNG, BMP, PDF, and TIFF.
-* For PDF and TIFF files, up to 2,000 pages (only the first two pages for the free tier) are processed.
-* The file size of images must be less than 500 MB (4 MB for the free tier) with dimensions at least 50 x 50 pixels and at most 10,000 x 10,000 pixels. PDF files don't have a size limit.
+* For PDF and TIFF files, up to 2,000 pages are processed (only the first two pages for the free tier).
+* The file size of images must be less than 500 MB (4 MB for the free tier) with dimensions of at least 50 x 50 pixels and at most 10,000 x 10,000 pixels. PDF files don't have a size limit.
 * The minimum height of the text to be extracted is 12 pixels for a 1024 x 768 image, which corresponds to about 8-point font text at 150 DPI.
 
 >[!NOTE]
@@ -86,6 +86,6 @@ As with all of the Azure AI services, developers using the Azure AI Vision servi
 
 ## Next steps
 
-- OCR for general (non-document) images: try the [Azure AI Vision 4.0 preview Image Analysis REST API quickstart](./concept-ocr.md).
-- OCR for PDF, Office and HTML documents and document images: start with [Document Intelligence Read](../../ai-services/document-intelligence/concept-read.md).
-- Looking for the previous GA version? Refer to the [Azure AI Vision 3.2 GA SDK or REST API quickstarts](./quickstarts-sdk/client-library.md).
+- For OCR with general (non-document) images, try the [Azure AI Vision 4.0 preview Image Analysis REST API quickstart](./concept-ocr.md).
+- For OCR with PDF, Office, and HTML documents, as well as document images, start with [Document Intelligence Read](../../ai-services/document-intelligence/concept-read.md).
+- For the previous GA version, see the [Azure AI Vision 3.2 GA SDK or REST API quickstarts](./quickstarts-sdk/client-library.md).

articles/ai-services/content-understanding/quickstart/use-ai-foundry.md

@@ -18,7 +18,7 @@ This quickstart shows you how to use the Content Understanding service in the [*
 
 Suppose you have files—such as documents, images, audio, or video—and you want to automatically extract key information from them. With Content Understanding, you can create a task to organize your data processing, define a field schema that specifies the information to extract or generate, and then build an analyzer. The analyzer becomes an API endpoint that you can integrate into your applications or workflows.
 
-In this guide, we walk you through building and testing an analyzer for your scenario. You can start from scratch or use suggested templates for common use cases.
+In this guide, you build and test an analyzer for your scenario. You can start from scratch or use suggested templates for common use cases.
 
 :::image type="content" source="../media/overview/component-overview-updated.png" alt-text="Screenshot of Content Understanding overview, process, and workflow." lightbox="../media/overview/component-overview-updated.png" :::
 
@@ -28,27 +28,27 @@ To get started, make sure you have the following resources and permissions:
 
 * An Azure subscription. If you don't have an Azure subscription, [create a free account](https://azure.microsoft.com/free/).
 
-* An [Azure AI Foundry hub-based project](../../../ai-foundry/how-to/create-projects.md) created in one of the following [supported regions](../service-limits.md): `westus`, `swedencentral`, or `australiaeast`. A project is used to organize your work and save state while building customized AI apps. You can create a project from the [home page of AI Foundry](https://aka.ms/foundry-home-page), or the [Content Understanding landing page](https://aka.ms/cu-landing).
+* An [Azure AI Foundry hub-based project](../../../ai-foundry/how-to/create-projects.md) created in one of the following [supported regions](../service-limits.md): `westus`, `swedencentral`, or `australiaeast`. Use a project to organize your work and save state while building customized AI apps. You can create a project from the [home page of AI Foundry](https://aka.ms/foundry-home-page), or the [Content Understanding landing page](https://aka.ms/cu-landing).
 
 [!INCLUDE [hub based project required](../../../ai-foundry/includes/uses-hub-only.md)]
 
 ## Create your single-file task powered by Content Understanding Standard mode
 
-Follow these steps to create a custom task in the Azure AI Foundry. This task is used to build your first analyzer.
+Follow these steps to create a custom task in the Azure AI Foundry. Use this task to build your first analyzer.
 
 1. Go to the **Home** page of [Azure AI Foundry](https://ai.azure.com/?cid=learnDocs).
 1. Select your hub based project. You might need to select **View all resources** to see your project.
 1. Select **Content Understanding** from the left navigation pane.
 1. Select **+ Create**.
-2. In this guide, you create a `Single-file task` utilizing Content Understanding Standard mode, but if you're interested in creating a multi-file task utilizing Pro mode, refer to [Create an Azure AI Content Understanding multi-file task in the Azure AI Foundry portal](./use-ai-foundry-pro-mode.md). For more information on which mode is right for your scenario, check out [Azure AI Content Understanding pro and standard modes](../concepts/standard-pro-modes.md).
+1. In this guide, you create a `Single-file task` utilizing Content Understanding Standard mode, but if you're interested in creating a multi-file task utilizing Pro mode, refer to [Create an Azure AI Content Understanding multi-file task in the Azure AI Foundry portal](./use-ai-foundry-pro-mode.md). For more information on which mode is right for your scenario, check out [Azure AI Content Understanding pro and standard modes](../concepts/standard-pro-modes.md).
 1. Enter a name for your task. Optionally, enter a description and change other settings.
 1. Select **Create**.
 
 ## Create your first analyzer
 
-Now that everything is configured to get started, we can walk through how to build your first analyzer. 
+Now that everything is configured, you can build your first analyzer. 
 
-When you create a single-file Content Understanding task, you start by uploading a sample of your data and building your field schema. The schema is the customizable framework that allows the analyzer to extract insights from your data. In this example, the schema is created to extract key data from an invoice document, but you can bring in any type of data and the steps remain the same. For a complete list of supported file types, see [input file limits](../service-limits.md#input-file-limits).
+When you create a single-file Content Understanding task, you start by uploading a sample of your data and building your field schema. The schema is the customizable framework that allows the analyzer to extract insights from your data. In this example, you create the schema to extract key data from an invoice document, but you can bring in any type of data and the steps remain the same. For a complete list of supported file types, see [input file limits](../service-limits.md#input-file-limits).
 
 1. Upload a [sample file of an invoice document](https://github.com/Azure-Samples/azure-ai-content-understanding-python/raw/refs/heads/main/data/invoice.pdf) or any other data relevant to your scenario.
 
@@ -60,7 +60,7 @@ When you create a single-file Content Understanding task, you start by uploading
 
    :::image type="content" source="../media/quickstarts/invioce-template.png" alt-text="Screenshot of analyzer templates.":::
 
-1. Next, you can add fields to your schema to reflect all of the outputs you want to generate. 
+1. Next, add fields to your schema to reflect all of the outputs you want to generate. 
 
     * Specify clear and simple field names. Some example fields might include **vendorName**, **items**, **price**.
 
@@ -70,29 +70,29 @@ When you create a single-file Content Understanding task, you start by uploading
 
     * Specify the method to generate the value for each field.
 
-      For best practices on how to define your field schema, refer to [best practices for Azure AI Content Understanding](../concepts//best-practices.md). It may take a few minutes to build out your schema.
+      For best practices on how to define your field schema, refer to [best practices for Azure AI Content Understanding](../concepts//best-practices.md). It might take a few minutes to build out your schema.
 
-1. Once you feel that the schema is ready to test, select **Save**. You can always come back and make changes if needed.
+1. When your schema is ready to test, select **Save**. You can always come back and make changes if needed.
 
    :::image type="content" source="../media/quickstarts/define-invoice-schema.png" alt-text="Screenshot of completed schema.":::
 
 1. With the completed schema, Content Understanding now generates the output on your sample data. At this step, you can add more data to test the analyzer's accuracy or make changes to the schema if needed.
 
    :::image type="content" source="../media/quickstarts/test-invoice.png" alt-text="Screenshot of schema testing step.":::
 
-1. Once you're satisfied with the quality of your output, select **Build analyzer**. This action creates an analyzer ID that you can integrate into your own applications, allowing you to call the analyzer from your code.
+1. When you're satisfied with the quality of your output, select **Build analyzer**. This action creates an analyzer ID that you can integrate into your own applications, allowing you to call the analyzer from your code.
 
    :::image type="content" source="../media/quickstarts/build-invoice-analyzer.png" alt-text="Screenshot of built analyzer.":::
 
-Now you successfully built your first Content Understanding analyzer, and are ready to start extracting insights from your data. Check out [Quickstart: Azure AI Content Understanding REST APIs](./use-rest-api.md) to utilize the REST API to call your analyzer.
+You've successfully built your first Content Understanding analyzer and are ready to start extracting insights from your data. Check out [Quickstart: Azure AI Content Understanding REST APIs](./use-rest-api.md) to utilize the REST API to call your analyzer.
 
 ## Sharing your project
 
-In order to share and manage access to the project you created, navigate to the Management Center, found at the bottom of the navigation for your project:
+To share the project you created and manage access, go to the Management Center. You can find it at the bottom of the navigation pane for your project:
 
   :::image type="content" source="../media/quickstarts/cu-landing-page.png" alt-text="Screenshot of where to find management center.":::
 
-You can manage the users and their individual roles here:
+In the Management Center, you can manage users and assign individual roles:
 
    :::image type="content" source="../media/quickstarts/management-center.png" alt-text="Screenshot of Project users section of management center.":::