Merge pull request #194894 from laujan/194726-review-jaep-read-how-to

194726 review jaep read how to

Author: laurabren

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

@@ -15,15 +15,15 @@ ms.custom: ignite-fall-2021
 
 # Form Recognizer read model
 
-The prebuilt-read model extracts printed and handwritten textual elements including lines, words, locations, and detected languages from documents (PDF and TIFF) and images (JPG, PNG, and BMP). The read model is the foundation for all Form Recognizer models. Layout, general document, custom, and prebuilt models use the prebuilt-read model as a basis for extracting text from documents.
+The Form Recognizer v3.0 preview includes the new Read API. Read extracts printed and handwritten from documents. The read model can detect lines, words, locations, and languages and is the core of all the other Form Recognizer models. Layout, general document, custom, and prebuilt models all use the read model as a foundation for extracting texts from documents.
 
 ## Development options
 
 The following resources are supported by Form Recognizer v3.0:
 
 | Feature | Resources | Model ID |
 |----------|------------|------------|
-|**Read model**| <ul><li>[**Form Recognizer Studio**](https://formrecognizer.appliedai.azure.com)</li><li>[**REST API**](https://westus.dev.cognitive.microsoft.com/docs/services/form-recognizer-api-v3-0-preview-1/operations/AnalyzeDocument)</li><li>[**C# SDK**](quickstarts/try-v3-csharp-sdk.md)</li><li>[**Python SDK**](quickstarts/try-v3-python-sdk.md)</li><li>[**Java SDK**](quickstarts/try-v3-java-sdk.md)</li><li>[**JavaScript SDK**](quickstarts/try-v3-javascript-sdk.md)</li></ul>|**prebuilt-read**|
+|**Read model**| <ul><li>[**Form Recognizer Studio**](https://formrecognizer.appliedai.azure.com)</li><li>[**REST API**](how-to-guides/use-prebuilt-read.md?pivots=programming-language-rest-api)</li><li>[**C# SDK**](how-to-guides/use-prebuilt-read.md?pivots=programming-language-csharp)</li><li>[**Python SDK**](how-to-guides/use-prebuilt-read.md?pivots=programming-language-python)</li><li>[**Java SDK**](how-to-guides/use-prebuilt-read.md?pivots=programming-language-java)</li><li>[**JavaScript**](how-to-guides/use-prebuilt-read.md?pivots=programming-language-javascript)</li></ul>|**prebuilt-read**|
 
 ## Data extraction
 
@@ -77,7 +77,7 @@ Form Recognizer preview version supports several languages for the read model. *
 
 ### Text lines and words
 
-Read API extracts text from documents and images with multiple text angles and colors. It accepts photos of documents, faxes, printed and/or handwritten (English only) text, and mixed modes. Text is extracted with information provided on lines, words, bounding boxes, confidence scores, and style (handwritten or other).
+Read API extracts text from documents and images with multiple text angles and colors. It accepts photos of documents, faxes, printed and/or handwritten (English only) text, and mixed modes. Text is extracted from data provided in lines, words, bounding boxes, confidence scores, and style.
 
 ### Language detection (v3.0 preview)
 
@@ -93,12 +93,17 @@ For large multi-page documents, use the `pages` query parameter to indicate spec
 
 ## Next steps
 
-* Complete a Form Recognizer quickstart:
+Complete a Form Recognizer quickstart:
 
-  > [!div class="nextstepaction"]
-  > [Form Recognizer quickstart](quickstarts/try-sdk-rest-api.md)
+> [!div class="checklist"]
+>
+> * [**REST API**](how-to-guides/use-prebuilt-read.md?pivots=programming-language-rest-api)
+> * [**C# SDK**](how-to-guides/use-prebuilt-read.md?pivots=programming-language-csharp)
+> * [**Python SDK**](how-to-guides/use-prebuilt-read.md?pivots=programming-language-python)
+> * [**Java SDK**](how-to-guides/use-prebuilt-read.md?pivots=programming-language-java)
+> * [**JavaScript**](how-to-guides/use-prebuilt-read.md?pivots=programming-language-javascript)</li></ul>
 
-* Explore our REST API:
+Explore our REST API:
 
-    > [!div class="nextstepaction"]
-    > [Form Recognizer API v3.0](https://westus.dev.cognitive.microsoft.com/docs/services/form-recognizer-api-v3-0-preview-2/operations/AnalyzeDocument)
+> [!div class="nextstepaction"]
+> [Form Recognizer API v3.0](https://westus.dev.cognitive.microsoft.com/docs/services/form-recognizer-api-v3-0-preview-2/operations/AnalyzeDocument)

articles/applied-ai-services/form-recognizer/how-to-guides/build-custom-model-v3.md

@@ -7,7 +7,7 @@ manager: nitinme
 ms.service: applied-ai-services
 ms.subservice: forms-recognizer
 ms.topic: how-to
-ms.date: 02/16/2022
+ms.date: 04/13/2022
 ms.author: lajanuar
 ---
 
@@ -32,15 +32,15 @@ Follow these tips to further optimize your data set for training:
 
 ## Upload your training data
 
-When you've put together the set of forms or documents that you'll use 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'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.
 
 ## Create a project in the Form Recognizer Studio
 
-The Form Recognizer Studio provides and orchestrates all the API calls required to create the files required to complete your dataset and train your model.
+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). If this is your first time using the Studio, you'll need to [initialize it for use](../quickstarts/try-v3-form-recognizer-studio.md). Follow the [additional prerequisite 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'll need to [initialize your subscription, resource group, and resource](../quickstarts/try-v3-form-recognizer-studio.md). Follow the [additional prerequisite 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. In the Studio select the **Custom models** tile, on the custom models page and select the **Create a project** button.
+1. In the Studio, select the **Custom models** tile, on the custom models page and select the **Create a project** button.
 
     :::image type="content" source="../media/how-to/studio-create-project.png" alt-text="Screenshot: Create a project in the Form Recognizer Studio.":::
 
@@ -53,7 +53,7 @@ The Form Recognizer Studio provides and orchestrates all the API calls required
 
     :::image type="content" source="../media/how-to/studio-select-resource.png" alt-text="Screenshot: Select the Form Recognizer resource.":::
 
-1. Next select the storage account where you uploaded the dataset you wish to use to train your custom model. The **Folder path** should be empty if your training documents are in the root of the container. If your documents are in a sub-folder, enter the relative path from the container root in the **Folder path** field. Once your storage account is configured, select continue.
+1. Next select the storage account where you uploaded your custom model training dataset. The **Folder path** should be empty if your training documents are in the root of the container. If your documents are in a subfolder, enter the relative path from the container root in the **Folder path** field. Once your storage account is configured, select continue.
 
     :::image type="content" source="../media/how-to/studio-select-storage.png" alt-text="Screenshot: Select the storage account.":::
 
@@ -71,13 +71,13 @@ You'll see the files you uploaded to storage on the left of your screen, with th
 
 1. Enter a name for the field.
 
-1. To assign a value to the field, simply 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. You'll see the labeled value below the field name in the list of fields.
 
-1. Repeat this process for all the fields you wish to label for your dataset
+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 in the document list and selecting the text to be labeled
+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 an additional fields.json file. This is the training dataset that will be submitted to train the model.
+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.
 
 ## Train your model
 
@@ -119,4 +119,4 @@ Congratulations you've trained a custom model in the Form Recognizer Studio! You
 > [Learn about custom model types](../concept-custom.md)
 
 > [!div class="nextstepaction"]
-> [Learn about accuracy and confidence with custom models](../concept-accuracy-confidence.md)
+> [Learn about accuracy and confidence with custom models](../concept-accuracy-confidence.md)