Merge pull request #242768 from laujan/azure-ai-migration-update-v3-links

azure ai migration update

Author: JamesJBarnett

articles/applied-ai-services/.openpublishing.redirection.applied-ai-services.json

@@ -167,4 +167,4 @@
             "redirect_document_id": true
           }
      ]
-}
+}

articles/applied-ai-services/form-recognizer/choose-model-feature.md

@@ -59,4 +59,4 @@ The following decision charts highlight the features of each **Form Recognizer v
 
 ## Next steps
 
-* [Learn how to process your own forms and documents](quickstarts/try-v3-form-recognizer-studio.md) with the [Form Recognizer Studio](https://formrecognizer.appliedai.azure.com/studio)
+* [Learn how to process your own forms and documents](quickstarts/try-form-recognizer-studio.md) with the [Form Recognizer Studio](https://formrecognizer.appliedai.azure.com/studio)

articles/applied-ai-services/form-recognizer/concept-accuracy-confidence.md

@@ -76,4 +76,4 @@ Variances in the visual structure of your documents affect the accuracy of your
 ## Next step
 
 > [!div class="nextstepaction"]
-> [Learn to create custom models ](quickstarts/try-v3-form-recognizer-studio.md#custom-models)
+> [Learn to create custom models ](quickstarts/try-form-recognizer-studio.md#custom-models)

articles/applied-ai-services/form-recognizer/concept-form-recognizer-studio.md

@@ -16,7 +16,7 @@ monikerRange: 'form-recog-3.0.0'
 
 **This article applies to:** ![Form Recognizer v3.0 checkmark](media/yes-icon.png) **Form Recognizer v3.0**.
 
-[Form Recognizer Studio](https://formrecognizer.appliedai.azure.com/) is an online tool for visually exploring, understanding, and integrating features from the Form Recognizer service into your applications. Use the [Form Recognizer Studio quickstart](quickstarts/try-v3-form-recognizer-studio.md) to get started analyzing documents with pretrained models. Build custom template models and reference the models in your applications using the [Python SDK v3.0](quickstarts/get-started-sdks-rest-api.md?view=form-recog-3.0.0&preserve-view=true) and other quickstarts.
+[Form Recognizer Studio](https://formrecognizer.appliedai.azure.com/) is an online tool for visually exploring, understanding, and integrating features from the Form Recognizer service into your applications. Use the [Form Recognizer Studio quickstart](quickstarts/try-form-recognizer-studio.md) to get started analyzing documents with pretrained models. Build custom template models and reference the models in your applications using the [Python SDK v3.0](quickstarts/get-started-sdks-rest-api.md?view=form-recog-3.0.0&preserve-view=true) and other quickstarts.
 
 The following image shows the Invoice prebuilt model feature at work.
 
@@ -43,4 +43,4 @@ The following Form Recognizer service features are available in the Studio.
 * Refer to our [**v3.0 REST API quickstarts**](quickstarts/get-started-sdks-rest-api.md?view=form-recog-3.0.0&preserve-view=true) to try the v3.0features using the new REST API.
 
 > [!div class="nextstepaction"]
-> [Form Recognizer Studio  quickstart](quickstarts/try-v3-form-recognizer-studio.md)
+> [Form Recognizer Studio  quickstart](quickstarts/try-form-recognizer-studio.md)

articles/applied-ai-services/form-recognizer/concept-layout.md

@@ -7,7 +7,7 @@ manager: nitinme
 ms.service: applied-ai-services
 ms.subservice: forms-recognizer
 ms.topic: conceptual
-ms.date: 05/23/2023
+ms.date: 06/23/2023
 ms.author: lajanuar
 ---
 
@@ -447,7 +447,7 @@ See here for a [sample document file](https://github.com/Azure-Samples/cognitive
 The JSON output has two parts:
 
 * `readResults` node contains all of the recognized text and selection mark. The text presentation hierarchy is page, then line, then individual words.
-* `pageResults` node contains the tables and cells extracted with their bounding boxes, confidence, and a reference to the lines and words in "readResults".
+* `pageResults` node contains the tables and cells extracted with their bounding boxes, confidence, and a reference to the lines and words in "readResults" field.
 
 ## Example Output
 
@@ -474,7 +474,7 @@ Layout API also extracts selection marks from documents. Extracted selection mar
 
 ::: moniker range="form-recog-3.0.0"
 
-* [Learn how to process your own forms and documents](quickstarts/try-v3-form-recognizer-studio.md) with the [Form Recognizer Studio](https://formrecognizer.appliedai.azure.com/studio)
+* [Learn how to process your own forms and documents](quickstarts/try-form-recognizer-studio.md) with the [Form Recognizer Studio](https://formrecognizer.appliedai.azure.com/studio)
 
 * Complete a [Form Recognizer quickstart](quickstarts/get-started-sdks-rest-api.md?view=form-recog-3.0.0&preserve-view=true) and get started creating a document processing app in the development language of your choice.
 

articles/applied-ai-services/form-recognizer/faq.yml

@@ -7,7 +7,7 @@ metadata:
   ms.service: applied-ai-services
   ms.subservice: forms-recognizer
   ms.topic: faq
-  ms.date: 03/03/2023
+  ms.date: 06/23/2023
   ms.author: lajanuar
   monikerRange: '>=form-recog-2.1.0'
 
@@ -85,7 +85,7 @@ sections:
 
           Models have the same lifecycle as the API version used to train the model. Custom models trained with a generally available (GA) version of the API have the same lifecycle as the API version. When the API version is deprecated, the model is no longer available for inference. Models trained with a preview version of the API also have the same lifecycle as the preview API. 
           
-          Expect that preview APIs will be deprecated within three months of an updated preview API version or newer generally available API version.
+          Expect preview API deprecation within three months of an updated preview API version or newer generally available API version.
           
       - question: |
            What is the accuracy score and how is it calculated?
@@ -167,7 +167,7 @@ sections:
 
           - [C#/.NET](https://www.nuget.org/packages/Azure.AI.FormRecognizer/4.0.0)
 
-          - [Java](https://oss.sonatype.org/#nexus-search;quick-azure-ai-formrecognizer)
+          - [Java](https://oss.sonatype.org/#nexus-search; quick-azure-ai-formrecognizer)
 
           - [JavaScript/TypeScript](https://www.npmjs.com/package/@azure/ai-form-recognizer/v/4.0.0-beta.4)
 
@@ -186,7 +186,7 @@ sections:
             | JavaScript | [4.0.0](https://azuresdkdocs.blob.core.windows.net/$web/javascript/azure-ai-form-recognizer/4.0.0/index.html)|[**v3.0**](https://westus.dev.cognitive.microsoft.com/docs/services/form-recognizer-api-2022-08-31/operations/AnalyzeDocument)</br> [**v2.1**](https://westus.dev.cognitive.microsoft.com/docs/services/form-recognizer-api-v2-1/operations/AnalyzeBusinessCardAsync)</br>[**v2.0**](https://westus.dev.cognitive.microsoft.com/docs/services/form-recognizer-api-v2/operations/AnalyzeLayoutAsync) |
             | Python | [3.2.0](https://azuresdkdocs.blob.core.windows.net/$web/python/azure-ai-formrecognizer/3.2.0/index.html) |[**v3.0**](https://westus.dev.cognitive.microsoft.com/docs/services/form-recognizer-api-2022-08-31/operations/AnalyzeDocument)</br> [**v2.1**](https://westus.dev.cognitive.microsoft.com/docs/services/form-recognizer-api-v2-1/operations/AnalyzeBusinessCardAsync)</br>[**v2.0**](https://westus.dev.cognitive.microsoft.com/docs/services/form-recognizer-api-v2/operations/AnalyzeLayoutAsync) |
 
-           For more information, *see* [**Supported clients**](sdk-overview.md#supported-clients)
+           For more information, *see* [Supported clients](sdk-overview.md#supported-clients)
 
 
       - question: |
@@ -371,7 +371,7 @@ sections:
       - question: |
             I'm building a custom model, what does the signature-detection label return?
         answer: |
-            [Signature detection](quickstarts/try-v3-form-recognizer-studio.md#signature-detection) looks for the presence of a signature, not the identity of the person signing the document.
+            [Signature detection](quickstarts/try-form-recognizer-studio.md#signature-detection) looks for the presence of a signature, not the identity of the person signing the document.
 
             If the model returns "unsigned" for signature detection, the model didn’t find a signature in the defined field.
 
@@ -386,7 +386,7 @@ sections:
 
              - Do your tables span across multiple pages? If so, to avoid having to label all of the pages, split the PDF into pages prior to sending it to Form Recognizer. Following the analysis, post-process the pages to a single table.
 
-             - If you’re creating custom models, refer to [Labeling as tables](quickstarts/try-v3-form-recognizer-studio.md#labeling-as-tables). Dynamic tables have a variable number of rows for each given column. Fixed tables have a constant number of rows for each given column.
+             - If you’re creating custom models, refer to [Labeling as tables](quickstarts/try-form-recognizer-studio.md#labeling-as-tables). Dynamic tables have a variable number of rows for each given column. Fixed tables have a constant number of rows for each given column.
 
       - question: |
             How can I move my trained models from one environment (like beta) to another (like production)?
@@ -464,7 +464,7 @@ sections:
           Form Recognizer connected containers send billing information to Azure by using a Form Recognizer resource on your Azure account. Connected containers don't send customer data, such as the image or text that's being analyzed, to Microsoft. See the [Cognitive Services container FAQ](../../cognitive-services/containers/disconnected-container-faq.yml#how-does-billing-work) for an example of the information sent to Microsoft for billing.
 
       - question: |
-           I received an "OutOfQuota" error message: "Container isn't in a valid state. Subscription validation failed with status 'OutOfQuota'. API key is out of quota".
+           I received an "OutOfQuota" error message: *Container isn't in a valid state. Subscription validation failed with status 'OutOfQuota' API key is out of quota*.
         answer: |
 
           Form Recognizer connected containers send billing information to Azure by using a Form Recognizer resource on your Azure account. You could get this message if the containers can't communicate with the billing endpoint.

articles/applied-ai-services/form-recognizer/how-to-guides/build-a-custom-classifier.md

@@ -45,7 +45,7 @@ Once you've put together the set of forms or documents for training, you need to
 
 The Form Recognizer Studio provides and orchestrates all the API calls required to complete your dataset and train your model.
 
-1. Start by navigating to the [Form Recognizer Studio](https://formrecognizer.appliedai.azure.com/studio). The first time you use the Studio, you need to [initialize your subscription, resource group, and resource](../quickstarts/try-v3-form-recognizer-studio.md). Then, follow the [prerequisites for custom projects](../quickstarts/try-v3-form-recognizer-studio.md#additional-prerequisites-for-custom-projects) to configure the Studio to access your training dataset.
+1. Start by navigating to the [Form Recognizer Studio](https://formrecognizer.appliedai.azure.com/studio). The first time you use the Studio, you need to [initialize your subscription, resource group, and resource](../quickstarts/try-form-recognizer-studio.md). Then, follow the [prerequisites for custom projects](../quickstarts/try-form-recognizer-studio.md#added-prerequisites-for-custom-projects) to configure the Studio to access your training dataset.
 
 1. In the Studio, select the **Custom classification model** tile, on the custom models section of the page and select the **Create a project** button.
 

articles/applied-ai-services/form-recognizer/how-to-guides/build-a-custom-model.md

@@ -43,19 +43,19 @@ Follow these tips to further optimize your data set for training:
 
 ## Upload your training data
 
-Once you've put together the set of forms or documents for training, you'll need to upload it to an Azure blob storage container. If you don't know how to create an Azure storage account with a container, following the [Azure Storage quickstart for Azure portal](../../../storage/blobs/storage-quickstart-blobs-portal.md). You can use the free pricing tier (F0) to try the service, and upgrade later to a paid tier for production.
+Once you've put together the set of forms or documents for training, you need to upload it to an Azure blob storage container. If you don't know how to create an Azure storage account with a container, following the [Azure Storage quickstart for Azure portal](../../../storage/blobs/storage-quickstart-blobs-portal.md). You can use the free pricing tier (F0) to try the service, and upgrade later to a paid tier for production.
 
 ## Video: Train your custom model
 
-* Once you've gathered and uploaded your training dataset, you're ready to train your custom model. In the following video, we'll create a project and explore some of the fundamentals for successfully labeling and training a model.</br></br>
+* Once you've gathered and uploaded your training dataset, you're ready to train your custom model. In the following video, we create a project and explore some of the fundamentals for successfully labeling and training a model.</br></br>
 
   > [!VIDEO https://www.microsoft.com/en-us/videoplayer/embed/RE5fX1c]
 
 ## Create a project in the Form Recognizer Studio
 
 The Form Recognizer Studio provides and orchestrates all the API calls required to complete your dataset and train your model.
 
-1. Start by navigating to the [Form Recognizer Studio](https://formrecognizer.appliedai.azure.com/studio). The first time you use the Studio, you'll need to [initialize your subscription, resource group, and resource](../quickstarts/try-v3-form-recognizer-studio.md). Then, follow the [prerequisites for custom projects](../quickstarts/try-v3-form-recognizer-studio.md#additional-prerequisites-for-custom-projects) to configure the Studio to access your training dataset.
+1. Start by navigating to the [Form Recognizer Studio](https://formrecognizer.appliedai.azure.com/studio). The first time you use the Studio, you need to [initialize your subscription, resource group, and resource](../quickstarts/try-form-recognizer-studio.md). Then, follow the [prerequisites for custom projects](../quickstarts/try-form-recognizer-studio.md#added-prerequisites-for-custom-projects) to configure the Studio to access your training dataset.
 
 1. In the Studio, select the **Custom models** tile, on the custom models page and select the **Create a project** button.
 
@@ -78,23 +78,23 @@ The Form Recognizer Studio provides and orchestrates all the API calls required
 
 ## Label your data
 
-In your project, your first task is to label your dataset with the fields you wish to extract. 
+In your project, your first task is to label your dataset with the fields you wish to extract.
 
-You'll see the files you uploaded to storage on the left of your screen, with the first file ready to be labeled.
+The files you uploaded to storage are listed on the left of your screen, with the first file ready to be labeled.
 
 1. To start labeling your dataset, create your first field by selecting the plus (➕) button on the top-right of the screen to select a field type. 
 
     :::image type="content" source="../media/how-to/studio-create-label.png" alt-text="Screenshot: Create a label.":::
 
 1. Enter a name for the field.
 
-1. To assign a value to the field, choose a word or words in the document and select the field in either the dropdown or the field list on the right navigation bar. You'll see the labeled value below the field name in the list of fields.
+1. To assign a value to the field, choose a word or words in the document and select the field in either the dropdown or the field list on the right navigation bar. The labeled value is below the field name in the list of fields.
 
 1. Repeat the process for all the fields you wish to label for your dataset.
 
 1. Label the remaining documents in your dataset by selecting each document and selecting the text to be labeled.
 
-You now have all the documents in your dataset labeled. If you look at the storage account, you'll find a *.labels.json* and *.ocr.json* files that correspond to each document in your training dataset and a new fields.json file. This training dataset will be submitted to train the model.
+You now have all the documents in your dataset labeled. The *.labels.json* and *.ocr.json* files correspond to each document in your training dataset and a new fields.json file. This training dataset is submitted to train the model.
 
 ## Train your model
 
@@ -173,21 +173,21 @@ Follow these tips to further optimize your data set for training.
 
 ## Upload your training data
 
-When you've put together the set of form documents that you'll use for training, you need to upload it to an Azure blob storage container. If you don't know how to create an Azure storage account with a container, follow the [Azure Storage quickstart for Azure portal](../../../storage/blobs/storage-quickstart-blobs-portal.md). Use the standard performance tier.
+When you've put together the set of form documents for training, you need to upload it to an Azure blob storage container. If you don't know how to create an Azure storage account with a container, follow the [Azure Storage quickstart for Azure portal](../../../storage/blobs/storage-quickstart-blobs-portal.md). Use the standard performance tier.
 
-If you want to use manually labeled data, you'll also have to upload the *.labels.json* and *.ocr.json* files that correspond to your training documents. You can use the [Sample Labeling tool](../label-tool.md) (or your own UI) to generate these files.
+If you want to use manually labeled data, upload the *.labels.json* and *.ocr.json* files that correspond to your training documents. You can use the [Sample Labeling tool](../label-tool.md) (or your own UI) to generate these files.
 
 ### Organize your data in subfolders (optional)
 
-By default, the [Train Custom Model](https://westus.dev.cognitive.microsoft.com/docs/services/form-recognizer-api-v2-1/operations/TrainCustomModelAsync) API will only use documents that are located at the root of your storage container. However, you can train with data in subfolders if you specify it in the API call. Normally, the body of the [Train Custom Model](https://westus.dev.cognitive.microsoft.com/docs/services/form-recognizer-api-v2-1/operations/TrainCustomModelAsync) call has the following format, where `<SAS URL>` is the Shared access signature URL of your container:
+By default, the [Train Custom Model](https://westus.dev.cognitive.microsoft.com/docs/services/form-recognizer-api-v2-1/operations/TrainCustomModelAsync) API only uses documents that are located at the root of your storage container. However, you can train with data in subfolders if you specify it in the API call. Normally, the body of the [Train Custom Model](https://westus.dev.cognitive.microsoft.com/docs/services/form-recognizer-api-v2-1/operations/TrainCustomModelAsync) call has the following format, where `<SAS URL>` is the Shared access signature URL of your container:
 
 ```json
 {
   "source":"<SAS URL>"
 }
 ```
 
-If you add the following content to the request body, the API will train with documents located in subfolders. The `"prefix"` field is optional and will limit the training data set to files whose paths begin with the given string. So a value of `"Test"`, for example, will cause the API to look at only the files or folders that begin with the word "Test".
+If you add the following content to the request body, the API trains with documents located in subfolders. The `"prefix"` field is optional and limits the training data set to files whose paths begin with the given string. So a value of `"Test"`, for example, causes the API to look at only the files or folders that begin with the word *Test*.
 
 ```json
 {