Merge pull request #285389 from MicrosoftDocs/main

Publish to live, Friday 4 AM PST, 8/23

Author: ttorble

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

@@ -7,24 +7,24 @@ author: PatrickFarley
 manager: nitinme
 ms.service: azure-ai-vision
 ms.topic: conceptual
-ms.date: 04/30/2024
+ms.date: 08/21/2024
 ms.author: pafarley
 ---
 
 # OCR for images (version 4.0)
 
 > [!NOTE]
 >
-> For extracting text from PDF, Office, and HTML documents and document images, use the [Document Intelligence Read OCR model](../../ai-services/document-intelligence/concept-read.md) optimized for text-heavy digital and scanned documents with an asynchronous API that makes it easy to power your intelligent document processing scenarios.
+> If you want to extract text from PDFs, Office files, or HTML documents and document images, use the [Document Intelligence Read OCR model](../../ai-services/document-intelligence/concept-read.md). It's optimized for text-heavy digital and scanned documents and uses an asynchronous API that makes it easy to power your intelligent document processing scenarios.
 
-OCR traditionally started as a machine-learning-based technique for extracting text from in-the-wild and non-document images like product labels, user-generated images, screenshots, street signs, and posters. For several scenarios, such as single images that aren't text-heavy, you need a fast, synchronous API or service. This allows OCR to be embedded in near real-time user experiences to enrich content understanding and follow-up user actions with fast turn-around times.
+OCR is a machine-learning-based technique for extracting text from in-the-wild and non-document images like product labels, user-generated images, screenshots, street signs, and posters. The Azure AI Vision OCR service provides a fast, synchronous API for lightweight scenarios where images aren't text-heavy. This allows OCR to be embedded in near real-time user experiences to enrich content understanding and follow-up user actions with fast turn-around times.
 
-## What is Computer Vision v4.0 Read OCR?
+## What is Azure AI Vision v4.0 Read OCR?
 
-The new Computer Vision Image Analysis 4.0 REST API offers the ability to extract printed or handwritten text from images in a unified performance-enhanced synchronous API that makes it easy to get all image insights including OCR results in a single API operation. The Read OCR engine is built on top of multiple deep learning models supported by universal script-based models for [global language support](./language-support.md).
+The new Azure AI Vision Image Analysis 4.0 REST API offers the ability to extract printed or handwritten text from images in a unified performance-enhanced synchronous API that makes it easy to get all image insights including OCR results in a single API operation. The Read OCR engine is built on top of multiple deep learning models supported by universal script-based models for [global language support](./language-support.md).
 
 > [!TIP]
-> You can use the OCR feature through the [Azure OpenAI](/azure/ai-services/openai/overview) service. The **GPT-4 Turbo with Vision** model lets you chat with an AI assistant that can analyze the images you share, and the Vision Enhancement option uses Image Analysis to give the AI assistance more details (readable text and object locations) about the image. For more information, see the [GPT-4 Turbo with Vision quickstart](/azure/ai-services/openai/gpt-v-quickstart).
+> You can also use the OCR feature in conjunction with the [Azure OpenAI](/azure/ai-services/openai/overview) service. The **GPT-4 Turbo with Vision** model lets you chat with an AI assistant that can analyze the images you share, and the Vision Enhancement option uses Image Analysis to give the AI assistant more details (readable text and object locations) about the image. For more information, see the [GPT-4 Turbo with Vision quickstart](/azure/ai-services/openai/gpt-v-quickstart).
 
 ## Text extraction example
 

articles/ai-services/computer-vision/includes/quickstarts-sdk/image-analysis-cpp-sdk-40.md

@@ -12,7 +12,7 @@ ms.author: pafarley
 
 <a name="HOLTop"></a>
 
-Use the Image Analysis client SDK for C++ to analyze an image to read text and generate an image caption. This quickstart calls a function `AnalyzeImage()`, which uses the client object to analyze a remote image and print the results to the console.
+Use the Image Analysis client SDK for C++ to read text from an image and generate an image caption. This quickstart calls a function, `AnalyzeImage()`, which uses the client object to analyze a remote image and print the results to the console.
 
 [Reference documentation](/cpp/cognitive-services/vision) | [Package (NuGet)](https://www.nuget.org/packages/Azure.AI.Vision.ImageAnalysis) | [Samples](https://github.com/Azure-Samples/azure-ai-vision-sdk/tree/main/samples/cpp/image-analysis)
 

articles/ai-services/computer-vision/includes/quickstarts-sdk/image-analysis-csharp-sdk-40.md

@@ -12,7 +12,7 @@ ms.author: pafarley
 
 <a name="HOLTop"></a>
 
-Use the Image Analysis client SDK for .NET to analyze an image to read text and generate an image caption. This quickstart analyzes a remote image and prints the results to the console. 
+Use the Image Analysis client SDK for .NET to read text in an image and generate an image caption. This quickstart analyzes a remote image and prints the results to the console.
 
 [Reference documentation](https://aka.ms/azsdk/image-analysis/ref-docs/csharp) | [Package (NuGet)](https://aka.ms/azsdk/image-analysis/package/nuget) | [Samples](https://aka.ms/azsdk/image-analysis/samples/csharp)
 

articles/ai-services/computer-vision/includes/quickstarts-sdk/image-analysis-java-sdk-40.md

@@ -12,7 +12,7 @@ ms.author: pafarley
 
 <a name="HOLTop"></a>
 
-Use the Image Analysis client SDK for Java to analyze an image to read text and generate an image caption. This quickstart analyzes a remote image and prints the results to the console. 
+Use the Image Analysis client SDK for Java to read text in an image and generate an image caption. This quickstart analyzes a remote image and prints the results to the console. 
 
 [Reference documentation](https://aka.ms/azsdk/image-analysis/ref-docs/java) | [Maven Package](https://aka.ms/azsdk/image-analysis/package/maven) | [Samples](https://aka.ms/azsdk/image-analysis/samples/java)
 
@@ -52,10 +52,6 @@ Open a console window and create a new folder for your quickstart application.
           <version>1.0.0-beta.2</version>
         </dependency>
         <!-- https://mvnrepository.com/artifact/org.slf4j/slf4j-nop -->
-        <!-- Optional: provide a slf4j implementation. Here we use a no-op implementation
-        just to make the slf4j console spew warning go away. We can still use the internal
-        logger in azure.core library. See
-        https://github.com/Azure/azure-sdk-for-java/tree/main/sdk/vision/azure-ai-vision-imageanalysis#enable-http-requestresponse-logging -->
         <dependency>
           <groupId>org.slf4j</groupId>
           <artifactId>slf4j-nop</artifactId>

articles/ai-services/computer-vision/includes/quickstarts-sdk/image-analysis-node-sdk-40.md

@@ -12,7 +12,7 @@ ms.author: pafarley
 
 <a name="HOLTop"></a>
 
-Use the Image Analysis client SDK for JavaScript to analyze an image to read text and generate an image caption. This quickstart analyzes a remote image and prints the results to the console.
+Use the Image Analysis client SDK for JavaScript to read text in an image and generate an image caption. This quickstart analyzes a remote image and prints the results to the console.
 
 [Reference documentation](https://aka.ms/azsdk/image-analysis/ref-docs/js) | [Package (npm)](https://aka.ms/azsdk/image-analysis/package/npm) | [Samples](https://aka.ms/azsdk/image-analysis/samples/js)
 

articles/ai-services/computer-vision/includes/quickstarts-sdk/image-analysis-python-sdk-40.md

@@ -12,7 +12,7 @@ ms.author: pafarley
 
 <a name="HOLTop"></a>
 
-Use the Image Analysis client SDK for Python to analyze an image to read text and generate an image caption. This quickstart analyzes a remote image and prints the results to the console.
+Use the Image Analysis client SDK for Python to read text in an image and generate an image caption. This quickstart analyzes a remote image and prints the results to the console.
 
 [Reference documentation](https://aka.ms/azsdk/image-analysis/ref-docs/python) | [Package (PyPi)](https://aka.ms/azsdk/image-analysis/package/pypi) | [Samples](https://aka.ms/azsdk/image-analysis/samples/python)
 

articles/ai-services/computer-vision/index.yml

@@ -1,16 +1,16 @@
 ### YamlMime:Hub
 
 title: Azure AI Vision documentation # < 60 chars
-summary: The cloud-based Azure AI Vision API provides developers with access to advanced algorithms for processing images and returning information. By uploading an image or specifying an image URL, Azure AI Vision algorithms can analyze visual content in different ways based on inputs and user choices. Learn how to analyze visual content in different ways with quickstarts, tutorials, and samples. # < 160 chars
+summary: The cloud-based Azure AI Vision service provides developers with access to advanced algorithms for processing images and returning information. By uploading an image or specifying an image URL, Azure AI Vision algorithms can analyze visual content in different ways based on inputs and user choices. Learn how to analyze visual content in different ways with quickstarts, tutorials, and samples. # < 160 chars
 
 metadata:
   title: Azure AI Vision documentation - Quickstarts, Tutorials, API Reference - Azure AI services | Microsoft Docs
-  description: The cloud-based Azure AI Vision API provides developers with access to advanced algorithms for processing images and returning information. By uploading an image or specifying an image URL, Azure AI Vision algorithms can analyze visual content in different ways based on inputs and user choices. Learn how to analyze visual content in different ways with quickstarts, tutorials, and samples.
+  description: The cloud-based Azure AI Vision service provides developers with access to advanced algorithms for processing images and returning information. By uploading an image or specifying an image URL, Azure AI Vision algorithms can analyze visual content in different ways based on inputs and user choices. Learn how to analyze visual content in different ways with quickstarts, tutorials, and samples.
   ms.service: azure-ai-vision
   ms.topic: hub-page
   author: PatrickFarley
   ms.author: pafarley
-  ms.date: 04/30/2024
+  ms.date: 08/21/2024
 
 highlightedContent:
   # itemType: architecture | concept | deploy | download | get-started | how-to-guide | learn | overview | quickstart | reference | tutorial | whats-new
@@ -111,7 +111,7 @@ conceptualContent:
           url: /training/modules/detect-analyze-faces/
         - itemType: reference
           text: Face API reference
-          url: /rest/api/face/
+          url: /rest/api/face/operation-groups
       footerLink:
         text: More
         url: overview-identity.md
@@ -170,7 +170,7 @@ additionalContent:
         - text: Image Analysis
           url: https://aka.ms/vision-4-0-ref
         - text: Face
-          url: /rest/api/face/
+          url: /rest/api/face/operation-groups
       - title: Resources
         links:
         - text: Pricing

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

@@ -8,7 +8,7 @@ manager: nitinme
 ms.service: azure-ai-vision
 ms.subservice: azure-ai-face
 ms.topic: overview
-ms.date: 04/30/2024
+ms.date: 08/21/2024
 ms.author: pafarley
 ms.custom:
   - ignite-2023
@@ -18,8 +18,7 @@ keywords: facial recognition, facial recognition software, facial analysis, face
 
 # What is the Azure AI Face service?
 
-
-The Azure AI Face service provides AI algorithms that detect, recognize, and analyze human faces in images. Facial recognition software is important in many different scenarios, such as identification, touchless access control, and face blurring for privacy.
+The Azure AI Face service provides AI algorithms that detect, recognize, and analyze human faces in images. Facial recognition software is important in many scenarios, such as identification, touchless access control, and automatic face blurring for privacy.
 
 You can use the Face service through a client library SDK or by calling the REST API directly. Follow the quickstart to get started.
 
@@ -45,9 +44,11 @@ For a more structured approach, follow a Training module for Face.
 
 ## Example use cases
 
-**Verify user identity**: Verify a person against a trusted face image. This verification could be used to grant access to digital or physical properties, such as a bank account, access to a building, and so on. In most cases, the trusted face image could come from a government-issued ID such as a passport or driver’s license, or it could come from an enrollment photo taken in person. During verification, liveness detection can play a critical role in verifying that the image comes from a real person, not a printed photo or mask. For more details on verification with liveness, see the [liveness tutorial](./Tutorials/liveness.md). For identity verification without liveness, follow the [quickstart](./quickstarts-sdk/identity-client-library.md).
+The following are common use cases for the Face service:
+
+**Verify user identity**: Verify a person against a trusted face image. This verification could be used to grant access to digital or physical properties such as a bank account, access to a building, and so on. In most cases, the trusted face image could come from a government-issued ID such as a passport or driver’s license, or it could come from an enrollment photo taken in person. During verification, liveness detection can play a critical role in verifying that the image comes from a real person, not a printed photo or mask. For more details on verification with liveness, see the [liveness tutorial](./Tutorials/liveness.md). For identity verification without liveness, follow the [quickstart](./quickstarts-sdk/identity-client-library.md).
 
-**Liveness detection**: Liveness detection is an anti-spoofing feature that checks whether a user is physically present in front of the camera. It's used to prevent spoofing attacks using a printed photo, video, or a 3D mask of the user's face. [Liveness tutorial](./Tutorials/liveness.md)
+**Liveness detection**: Liveness detection is an anti-spoofing feature that checks whether a user is physically present in front of the camera. It's used to prevent spoofing attacks using a printed photo, recorded video, or a 3D mask of the user's face. [Liveness tutorial](./Tutorials/liveness.md)
 
 **Touchless access control**: Compared to today’s methods like cards or tickets, opt-in face identification enables an enhanced access control experience while reducing the hygiene and security risks from card sharing, loss, or theft. Facial recognition assists the check-in process with a human in the loop for check-ins in airports, stadiums, theme parks, buildings, reception kiosks at offices, hospitals, gyms, clubs, or schools.
 
@@ -93,7 +94,7 @@ Face liveness SDK reference docs:
 - [Swift (iOS)](https://aka.ms/liveness-sdk-ios)
 - [JavaScript (Web)](https://aka.ms/liveness-sdk-web)
 
-## Face recognition
+## Face recognition operations
 
 Modern enterprises and apps can use the Face recognition technologies, including Face verification ("one-to-one" matching) and Face identification ("one-to-many" matching) to confirm that a user is who they claim to be.
 
@@ -132,13 +133,13 @@ And these images are the candidate faces:
 
 ![Five images of people smiling. Images A and B show the same person.](./media/FaceFindSimilar.Candidates.jpg)
 
-To find four similar faces, the **matchPerson** mode returns A and B, which show the same person as the target face. The **matchFace** mode returns A, B, C, and D, which is exactly four candidates, even if some aren't the same person as the target or have low similarity. For more information, see the [Facial recognition](concept-face-recognition.md) concepts guide or the [Find Similar API](/rest/api/face/face-recognition-operations/find-similar) reference documentation.
+To find four similar faces, the **matchPerson** mode returns A and B, which show the same person as the target face. The **matchFace** mode returns A, B, C, and D, which is exactly four candidates, even if some aren't the same person as the target or have low similarity. For more information, the [Find Similar API](/rest/api/face/face-recognition-operations/find-similar) reference documentation.
 
 ## Group faces
 
 The Group operation divides a set of unknown faces into several smaller groups based on similarity. Each group is a disjoint proper subset of the original set of faces. It also returns a single "messyGroup" array that contains the face IDs for which no similarities were found.
 
-All of the faces in a returned group are likely to belong to the same person, but there can be several different groups for a single person. Those groups are differentiated by another factor, such as expression, for example. For more information, see the [Facial recognition](concept-face-recognition.md) concepts guide or the [Group API](/rest/api/face/face-recognition-operations/group) reference documentation.
+All of the faces in a returned group are likely to belong to the same person, but there can be several different groups for a single person. Those groups are differentiated by another factor, such as expression, for example. For more information, see the [Group API](/rest/api/face/face-recognition-operations/group) reference documentation.
 
 ## Input requirements
 
@@ -160,4 +161,6 @@ As with all of the Azure AI services resources, developers who use the Face serv
 
 Follow a quickstart to code the basic components of a face recognition app in the language of your choice.
 
-- [Face quickstart](quickstarts-sdk/identity-client-library.md)
+> [!div class="nextstepaction"]
+> [Quickstart](quickstarts-sdk/identity-client-library.md)
+