update files

Author: laujan

articles/applied-ai-services/form-recognizer/api-v2-0/includes/csharp-v3-0-0.md

@@ -21,7 +21,7 @@ ms.custom: " devx-track-csharp"
 * The [Visual Studio IDE](https://visualstudio.microsoft.com/vs/) or current version of [.NET Core](https://dotnet.microsoft.com/download/dotnet-core).
 * An Azure Storage blob that contains a set of training data. See [Build a training data set for a custom model](../../build-training-data-set.md) for tips and options for putting together your training data set. For this quickstart, you can use the files under the **Train** folder of the [sample data set](https://go.microsoft.com/fwlink/?linkid=2090451) (download and extract *sample_data.zip*).
 * Once you have your Azure subscription, <a href="https://ms.portal.azure.com/#create/Microsoft.CognitiveServicesFormRecognizer"  title="Create a Form Recognizer resource"  target="_blank">create a Form Recognizer resource </a> in the Azure portal to get your key and endpoint. After it deploys, select **Go to resource**.
-  * You will need the key and endpoint from the resource you create to connect your application to the Form Recognizer API. You'll paste your key and endpoint into the code below later in the quickstart.
+  * You will need the key and endpoint from the resource you create to connect your application to the Form Recognizer API. Paste your key and endpoint into the code below later in the quickstart.
   * You can use the free pricing tier (`F0`) to try the service, and upgrade later to a paid tier for production.
 
 ## Setting up
@@ -68,7 +68,7 @@ In the application's **Program** class, create variables for your resource's key
 > [!IMPORTANT]
 > Go to the Azure portal. If the Form Recognizer resource you created in the **Prerequisites** section deployed successfully, click the **Go to Resource** button under **Next Steps**. You can find your key and endpoint in the resource's **key and endpoint** page, under **resource management**.
 >
-> Remember to remove the key from your code when you're done, and never post it publicly. For production, use secure methods to store and access your credentials. See the Cognitive Services [security](../../../../cognitive-services/cognitive-services-security.md) article for more information.
+> Remember to remove the key from your code when you're done, and never post it publicly. For production, use secure methods to store and access your credentials. For more information, _see_ Cognitive Services [security](../../../../cognitive-services/cognitive-services-security.md).
 
 [!code-csharp[](~/cognitive-services-quickstart-code/dotnet/FormRecognizer/FormRecognizerQuickstart.cs?name=snippet_creds)]
 
@@ -93,7 +93,7 @@ With Form Recognizer, you can create two different client types. The first, `For
 `FormTrainingClient` provides operations for:
 
 * Training custom models to analyze all fields and values found in your custom forms.  A `CustomFormModel` is returned indicating the form types the model will analyze, and the fields it will extract for each form type.
-* Training custom models to analyze specific fields and values you specify by labeling your custom forms.  A `CustomFormModel` is returned indicating the fields the model will extract, as well as the estimated accuracy for each field.
+* Training custom models to analyze specific fields and values you specify by labeling your custom forms.  A `CustomFormModel` is returned indicating the fields the model will extract and the estimated accuracy for each field.
 * Managing models created in your account.
 * Copying a custom model from one Form Recognizer resource to another.
 
@@ -116,7 +116,7 @@ These code snippets show you how to do the following tasks with the Form Recogni
 
 ## Authenticate the client
 
-Below **Main**, create a new method named `AuthenticateClient`. You'll use this in other tasks to authenticate your requests to the Form Recognizer service. This method uses the `AzureKeyCredential` object, so that if needed, you can update the API key without creating new client objects.
+Below **Main**, create a new method named `AuthenticateClient`. You'll use this method in other tasks to authenticate your requests to the Form Recognizer service. This method uses the `AzureKeyCredential` object, so that if needed, you can update the API key without creating new client objects.
 
 > [!IMPORTANT]
 > Get your key and endpoint from the Azure portal. If the Form Recognizer resource you created in the **Prerequisites** section deployed successfully, click the **Go to Resource** button under **Next Steps**. You can find your key and endpoint in the resource's **key and endpoint** page, under **resource management**.
@@ -131,7 +131,7 @@ Repeat the steps above for a new method that authenticates a training client.
 
 ## Get assets for testing
 
-You'll also need to add references to the URLs for your training and testing data. Add these to the root of your **Program** class.
+You'll also need to add references to the URLs for your training and testing data. Add these references to the root of your **Program** class.
 
 * To retrieve the SAS URL for your custom model training data, go to your storage resource in the Azure portal and select the **Storage Explorer** tab. Navigate to your container, right-click, and select **Get shared access signature**. It's important to get the SAS for your container, not for the storage account itself. Make sure the **Read**, **Write**, **Delete** and **List** permissions are checked, and select **Create**. Then copy the value in the **URL** section to a temporary location. It should have the form: `https://<storage account>.blob.core.windows.net/<container name>?<SAS value>`.
 
@@ -144,7 +144,7 @@ You'll also need to add references to the URLs for your training and testing dat
 
 ## Analyze layout
 
-You can use Form Recognizer to analyze tables, lines, and words in documents, without needing to train a model. The returned value is a collection of **FormPage** objects: one for each page in the submitted document. For more information about layout extraction see the [Layout conceptual guide](../../concept-layout.md).
+You can use Form Recognizer to analyze tables, lines, and words in documents, without needing to train a model. The returned value is a collection of **FormPage** objects: one for each page in the submitted document. For more information about layout extraction, see the [Layout conceptual guide](../../concept-layout.md).
 
 To analyze the content of a file at a given URL, use the `StartRecognizeContentFromUri` method.
 

articles/applied-ai-services/form-recognizer/api-v2-0/includes/java-v3-0-0.md

@@ -23,7 +23,7 @@ ms.author: lajanuar
 * The current version of the [Java Development Kit (JDK)](https://www.oracle.com/technetwork/java/javase/downloads/index.html)
 * The [Gradle build tool](https://gradle.org/install/), or another dependency manager.
 * Once you have your Azure subscription, <a href="https://ms.portal.azure.com/#create/Microsoft.CognitiveServicesFormRecognizer"  title="Create a Form Recognizer resource"  target="_blank">create a Form Recognizer resource </a> in the Azure portal to get your key and endpoint. After it deploys, select **Go to resource**.
-  * You will need the key and endpoint from the resource you create to connect your application to the Form Recognizer API. You'll paste your key and endpoint into the code below later in the quickstart.
+  * You will need the key and endpoint from the resource you create to connect your application to the Form Recognizer API. Paste your key and endpoint into the code below later in the quickstart.
   * You can use the free pricing tier (`F0`) to try the service, and upgrade later to a paid tier for production.
 * An Azure Storage blob that contains a set of training data. See [Build a training data set for a custom model](../../build-training-data-set.md) for tips and options for putting together your training data set. For this quickstart, you can use the files under the **Train** folder of the [sample data set](https://go.microsoft.com/fwlink/?linkid=2090451) (download and extract *sample_data.zip*).
 
@@ -37,7 +37,7 @@ In a console window (such as cmd, PowerShell, or Bash), create a new directory f
 mkdir myapp && cd myapp
 ```
 
-Run the `gradle init` command from your working directory. This command will create essential build files for Gradle, including *build.gradle.kts* which is used at runtime to create and configure your application.
+Run the `gradle init` command from your working directory. This command will create essential build files for Gradle, including *build.gradle.kts*, which is used at runtime to create and configure your application.
 
 ```console
 gradle init --type basic
@@ -92,9 +92,9 @@ In the application's **FormRecognizer** class, create variables for your resourc
 > [!IMPORTANT]
 > Go to the Azure portal. If the Form Recognizer resource you created in the **Prerequisites** section deployed successfully, click the **Go to Resource** button under **Next Steps**. You can find your key and endpoint in the resource's **key and endpoint** page, under **resource management**.
 >
-> Remember to remove the key from your code when you're done, and never post it publicly. For production, use secure methods to store and access your credentials. See the Cognitive Services [security](../../../../cognitive-services/cognitive-services-security.md) article for more information.
+> Remember to remove the key from your code when you're done, and never post it publicly. For production, use secure methods to store and access your credentials. For more information, _see_ Cognitive Services [security](../../../../cognitive-services/cognitive-services-security.md).
 
-In the application's **main** method, add calls for the methods used in this quickstart. You'll define these later. You'll also need to add references to the URLs for your training and testing data.
+In the application's **main** method, add calls for the methods used in this quickstart. You'll define these methods later. You'll also need to add references to the URLs for your training and testing data.
 
 * [!INCLUDE [get SAS URL](../../includes/sas-instructions.md)]
 
@@ -124,7 +124,7 @@ With Form Recognizer, you can create two different client types. The first, `For
 `FormTrainingClient` provides operations for:
 
 * Training custom models to analyze all fields and values found in your custom forms.  A `CustomFormModel` is returned indicating the form types the model will analyze, and the fields it will extract for each form type.
-* Training custom models to analyze specific fields and values you specify by labeling your custom forms.  A `CustomFormModel` is returned indicating the fields the model will extract, as well as the estimated accuracy for each field.
+* Training custom models to analyze specific fields and values you specify by labeling your custom forms.  A `CustomFormModel` is returned indicating the fields the model will extract and the estimated accuracy for each field.
 * Managing models created in your account.
 * Copying a custom model from one Form Recognizer resource to another.
 
@@ -151,7 +151,7 @@ At the top of your **main** method, add the following code. Here, you'll authent
 
 ## Analyze layout
 
-You can use Form Recognizer to analyze tables, lines, and words in documents, without needing to train a model. For more information about layout extraction see the [Layout conceptual guide](../../concept-layout.md).
+You can use Form Recognizer to analyze tables, lines, and words in documents, without needing to train a model. For more information about layout extraction, see the [Layout conceptual guide](../../concept-layout.md).
 
 To analyze the content of a file at a given URL, use the **beginRecognizeContentFromUrl** method.
 
@@ -306,7 +306,7 @@ You'll use the **beginRecognizeCustomFormsFromUrl** method.
 > [!TIP]
 > You can also analyze a local file. See the [FormRecognizerClient](/java/api/com.azure.ai.formrecognizer.formrecognizerclient) methods, such as **beginRecognizeCustomForms**. Or, see the sample code on [GitHub](https://github.com/Azure/azure-sdk-for-java/blob/master/sdk/formrecognizer/azure-ai-formrecognizer/src/samples/README.md) for scenarios involving local images.
 
-The returned value is a collection of **RecognizedForm** objects: one for each page in the submitted document.The following code prints the analysis results to the console. It prints each recognized field and corresponding value, along with a confidence score.
+The returned value is a collection of **RecognizedForm** objects: one for each page in the submitted document. The following code prints the analysis results to the console. It prints each recognized field and corresponding value, along with a confidence score.
 
 [!code-java[](~/cognitive-services-quickstart-code/java/FormRecognizer/FormRecognizer.java?name=snippet_analyze_print)]
 
@@ -398,7 +398,7 @@ If you want to clean up and remove a Cognitive Services subscription, you can de
 
 ## Troubleshooting
 
-From Recognizer clients raise `ErrorResponseException` exceptions. For example, if you try to provide an invalid file source URL an `ErrorResponseException` would be raised with an error indicating the failure cause. In the following code snippet, the error is handled gracefully by catching the exception and display the additional information about the error.
+Form Recognizer clients raise `ErrorResponseException` exceptions. For example, if you try to provide an invalid file source URL an `ErrorResponseException` would be raised with an error indicating the failure cause. In the following code snippet, the error is handled gracefully by catching the exception and display the additional information about the error.
 
 ```java Snippet:FormRecognizerBadRequest
 try {

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

@@ -16,7 +16,7 @@ ms.author: sajagtap
 >[!NOTE]
 > Form Recognizer Studio is currently in public preview. Some features may not be supported or have limited capabilities.
 
-[Form Recognizer Studio preview](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 with exploring the pre-trained models with sample documents or your own. Create projects to build custom form models and reference the models in your applications using the [Python SDK preview](quickstarts/try-v3-python-sdk.md) and other quickstarts.
+[Form Recognizer Studio preview](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 pre-trained models. Build custom form models and reference the models in your applications using the [Python SDK preview](quickstarts/try-v3-python-sdk.md) and other quickstarts.
 
 The following image shows the Invoice prebuilt model feature at work.
 
@@ -32,7 +32,7 @@ The following Form Recognizer service features are available in the Studio.
 
 * **Custom models**: Form Recognizer's custom models enable you to extract fields and values from models trained with your data, tailored to your forms and documents. Create standalone custom models or combine two or more custom models to create a composed model to extract data from multiple form types. Start with the [Studio Custom models quickstart](quickstarts/try-v3-form-recognizer-studio.md#custom-model-basics).  Use the online wizard, labeling interface, training step, and visualizations to understand how the feature works. Test the custom model with your sample documents and iterate to improve the model. See the [Custom models overview](concept-custom.md) to learn more and use the [Form Recognizer v3.0 preview migration guide](v3-migration-guide.md) to start integrating the new models with your applications.
 
-* **Custom models: Labeling features**: Form Recognizer Custom model creation requires identifying the fields to be extracted and labeling the content in your documents with those fields before training the custom models. Labeling text, selection marks, tabular data, and other content types are typically assisted with a user interface to ease the training workflow. For example, use the [Label as tables](quickstarts/try-v3-form-recognizer-studio.md#labeling-as-tables) and [Labeling for signature detection](quickstarts/try-v3-form-recognizer-studio.md#labeling-for-signature-detection) quickstarts to understand the labeling experience in Form Recognizer Studio.
+* **Custom models: Labeling features**: Form Recognizer Custom model creation requires identifying the fields to be extracted and labeling  those fields before training the custom models. Labeling text, selection marks, tabular data, and other content types are typically assisted with a user interface to ease the training workflow. For example, use the [Label as tables](quickstarts/try-v3-form-recognizer-studio.md#labeling-as-tables) and [Labeling for signature detection](quickstarts/try-v3-form-recognizer-studio.md#labeling-for-signature-detection) quickstarts to understand the labeling experience in Form Recognizer Studio.
 
 ## Next steps