update

Author: laujan

articles/applied-ai-services/form-recognizer/concept-business-card.md

@@ -123,7 +123,7 @@ See how data, including name, job title, address, email, and company name, is ex
 
 1. In the **key** field, paste  the key you obtained from your Form Recognizer resource.
 
-    :::image type="content" source="media/fott-select-form-type.png" alt-text="Screenshot: select form type dropdown window.":::
+    :::image type="content" source="media/fott-select-form-type.png" alt-text="Screenshot of the select-form-type dropdown menu.":::
 
 1. Select **Run analysis**. The Form Recognizer Sample Labeling tool will call the Analyze Prebuilt API and analyze the document.
 

articles/applied-ai-services/form-recognizer/concept-general-document.md

@@ -1,13 +1,13 @@
 ---
 title: General key-value extraction - Form Recognizer
 titleSuffix: Azure Applied AI Services
-description: Extract key-value paits, tables, selection marks,and text from your documents with Form Recognizer
+description: Extract key-value pairs, tables, selection marks, and text from your documents with Form Recognizer
 author: laujan
 manager: nitinme
 ms.service: applied-ai-services
 ms.subservice: forms-recognizer
 ms.topic: conceptual
-ms.date: 10/14/2022
+ms.date: 11/10/2022
 ms.author: lajanuar
 monikerRange: 'form-recog-3.0.0'
 recommendations: false
@@ -25,7 +25,7 @@ The General document v3.0 model combines powerful Optical Character Recognition
 The general document API supports most form types and will analyze your documents and extract keys and associated values. It's ideal for extracting common key-value pairs from documents. You can use the general document model as an alternative to training a custom model without labels.
 
 > [!NOTE]
-> The ```2022-06-30``` and later versions of the general document model add support for selection marks.
+> The ```2022-06-30``` and subsequent versions of the general document model add support for selection marks.
 
 ## General document features
 

articles/applied-ai-services/form-recognizer/concept-id-document.md

@@ -28,7 +28,7 @@ The Form Recognizer Identity document (ID) model combines Optical Character Reco
 
 ## Identity document (ID) processing
 
-Identity document (ID) processing involves extraction of data from identity documents whether manually or using OCR based techniques. Examples of identity documents include passports, driver licenses, resident cards, and national identity cards like the social security card in the US. It is an important step in any business process that requires some proof of identity. Examples include customer verification in banks and other financial institutions, mortgage applications, medical visits, claim processing, hospitality industry, and more. Individuals provide some proof of their identity via driver licenses, passports, and other similar documents so that the business can efficiently verify them before providing services and benefits.
+Identity document (ID) processing involves extraction of data from identity documents whether manually or using OCR based techniques. Examples of identity documents include passports, driver licenses, resident cards, and national identity cards like the social security card in the US. It's an important step in any business process that requires some proof of identity. Examples include customer verification in banks and other financial institutions, mortgage applications, medical visits, claim processing, hospitality industry, and more. Individuals provide some proof of their identity via driver licenses, passports, and other similar documents so that the business can efficiently verify them before providing services and benefits.
 
 ::: moniker range="form-recog-3.0.0"
 

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

@@ -27,7 +27,7 @@ The machine-learning-based invoice model combines powerful Optical Character Rec
 
 ## Automated invoice processing
 
-Automated invoice processing is the process of extracting key accounts payable fields from including invoice line items from invoices and integrating it with your accounts payable (AP) workflows for reviews and payments. Historically, the accounts payable process has been very manual and time consuming. Accurate extraction of key data from invoices is typically the first and one of the most critical steps in the invoice automation process.
+Automated invoice processing is the process of extracting key accounts payable fields from billing account documents. Extracted data includes line items from invoices integrated with your accounts payable (AP) workflows for reviews and payments. Historically, the accounts payable process has been done manually and, hence, very time consuming. Accurate extraction of key data from invoices is typically the first and one of the most critical steps in the invoice automation process.
 
 ::: moniker range="form-recog-3.0.0"
 
@@ -121,7 +121,7 @@ See how data, including customer information, vendor details, and line items, is
 
 1. In the **key** field, paste  the key you obtained from your Form Recognizer resource.
 
-    :::image type="content" source="media/fott-select-form-type.png" alt-text="Screenshot: select form type dropdown window.":::
+    :::image type="content" source="media/fott-select-form-type.png" alt-text="Screenshot showing the select-form-type dropdown menu.":::
 
 1. Select **Run analysis**. The Form Recognizer Sample Labeling tool will call the Analyze Prebuilt API and analyze the document.
 
@@ -233,17 +233,17 @@ The Invoice service will extract the text, tables, and 26 invoice fields. Follow
 | VendorName | string | Vendor who has created this invoice | CONTOSO LTD. | |
 | VendorAddress | string | Mailing address for the Vendor | 123 456th St New York, NY, 10001 | |
 | VendorAddressRecipient | string | Name associated with the VendorAddress | Contoso Headquarters | |
-| CustomerAddress | string | Mailing address for the Customer | 123 Other St, Redmond WA, 98052 | |
+| CustomerAddress | string | Mailing address for the Customer | 123 Other Street, Redmond WA, 98052 | |
 | CustomerAddressRecipient | string | Name associated with the CustomerAddress | Microsoft Corp | |
-| BillingAddress | string | Explicit billing address for the customer | 123 Bill St, Redmond WA, 98052 | |
+| BillingAddress | string | Explicit billing address for the customer | 123 Bill Street, Redmond WA, 98052 | |
 | BillingAddressRecipient | string | Name associated with the BillingAddress | Microsoft Services | |
-| ShippingAddress | string | Explicit shipping address for the customer | 123 Ship St, Redmond WA, 98052 | |
+| ShippingAddress | string | Explicit shipping address for the customer | 123 Ship Street, Redmond WA, 98052 | |
 | ShippingAddressRecipient | string | Name associated with the ShippingAddress | Microsoft Delivery | |
 | SubTotal | number | Subtotal field identified on this invoice | $100.00 | 100 |
 | TotalTax | number | Total tax field identified on this invoice | $10.00 | 10 |
 | InvoiceTotal | number | Total new charges associated with this invoice | $110.00 | 110 |
 | AmountDue |  number | Total Amount Due to the vendor | $610.00 | 610 |
-| ServiceAddress | string | Explicit service address or property address for the customer | 123 Service St, Redmond WA, 98052 | |
+| ServiceAddress | string | Explicit service address or property address for the customer | 123 Service Street, Redmond WA, 98052 | |
 | ServiceAddressRecipient | string | Name associated with the ServiceAddress | Microsoft Services | |
 | RemittanceAddress | string | Explicit remittance or payment address for the customer | 123 Remit St New York, NY, 10001 |  |
 | RemittanceAddressRecipient | string | Name associated with the RemittanceAddress | Contoso Billing |  |
@@ -262,7 +262,7 @@ Following are the line items extracted from an invoice in the JSON output respon
 | UnitPrice | number | The net or gross price (depending on the gross invoice setting of the invoice) of one unit of this item | $30.00 | 30 |
 | ProductCode | string| Product code, product number, or SKU associated with the specific line item | A123 | |
 | Unit | string| The unit of the line item, e.g,  kg, lb etc. | hours | |
-| Date | date| Date corresponding to each line item. Often it is a date the line item was shipped | 3/4/2021| 2021-03-04 |
+| Date | date| Date corresponding to each line item. Often it's a date the line item was shipped | 3/4/2021| 2021-03-04 |
 | Tax | number | Tax associated with each line item. Possible values include tax amount, tax %, and tax Y/N | 10% | |
 
 ### JSON output
@@ -271,7 +271,7 @@ The JSON output has three parts:
 
 * `"readResults"` node contains all of the recognized text and selection marks. Text is organized by page, then by line, then by 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".
-* `"documentResults"` node contains the invoice-specific values and line items that the model discovered. It is where you'll find all the fields from the invoice such as invoice ID, ship to, bill to, customer, total, line items and lots more.
+* `"documentResults"` node contains the invoice-specific values and line items that the model discovered. It's where you'll find all the fields from the invoice such as invoice ID, ship to, bill to, customer, total, line items and lots more.
 
 ## Migration guide
 

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

@@ -26,7 +26,7 @@ The Form Recognizer Layout is an advanced machine-learning based document layout
 
 ## Document layout analysis
 
-Document structure and layout analysis is the process of analyzing a document to extract regions of interest and their inter-relationships. The goal is to extract text and structural elements from the page for building better semantic understanding models. For all extracted text, there are two types of roles that text plays in a document layout.  Text, tables, and selection marks are examples of geometric roles. Titles, headings, and footers are examples of logical roles. For example. a reading system requires differentiating text regions from non-textual ones along with their reading order.
+Document structure layout analysis is the process of analyzing a document to extract regions of interest and their inter-relationships. The goal is to extract text and structural elements from the page for building better semantic understanding models. For all extracted text, there are two types of roles that text plays in a document layout.  Text, tables, and selection marks are examples of geometric roles. Titles, headings, and footers are examples of logical roles. For example, a reading system requires differentiating text regions from non-textual ones along with their reading order.
 
 The following illustration shows the typical components in an image of a sample page.
 
@@ -57,7 +57,7 @@ The following illustration shows the typical components in an image of a sample
 
 ### Data extraction
 
-**Starting with v3.0 GA**, it extracts paragraphs and additional structure information like titles, section headings, page header, page footer, page number, and footnote from the document page. These are examples of logical roles described in the previous section. This capability is supported for PDF documents and images (JPG, PNG, BMP, TIFF).
+**Starting with v3.0 GA**, it extracts paragraphs and more structure information like titles, section headings, page header, page footer, page number, and footnote from the document page. These structural elements are examples of logical roles described in the previous section. This capability is supported for PDF documents and images (JPG, PNG, BMP, TIFF).
 
 | **Model**   | **Text**   | **Selection Marks**   | **Tables**  | **Paragraphs** | **Logical roles** |
 | --- | --- | --- | --- | --- | --- |
@@ -99,21 +99,9 @@ The following tools are supported by Form Recognizer v2.1:
 
 ::: moniker-end
 
-Extract data, including text and table structure from documents.
+### Try layout extraction
 
-You'll need the following resources:
-
-* An Azure subscription—you can [create one for free](https://azure.microsoft.com/free/cognitive-services/)
-
-* A [Form Recognizer instance](https://portal.azure.com/#create/Microsoft.CognitiveServicesFormRecognizer) in the Azure portal. You can use the free pricing tier (`F0`) to try the service. After your resource deploys, select **Go to resource** to get your key and endpoint.
-
- :::image type="content" source="media/containers/keys-and-endpoint.png" alt-text="Screenshot: keys and endpoint location in the Azure portal.":::
-
-:: moniker range="form-recog-3.0.0"
-
-### Try Form Recognizer
-
-Try extracting data from forms and documents using the Form Recognizer Studio. You'll need the following resources:
+See how data, including text, tables, table headers, selection marks, and structure information is extracted from documents using  Form Recognizer. You'll need the following resources:
 
 * An Azure subscription—you can [create one for free](https://azure.microsoft.com/free/cognitive-services/)
 
@@ -189,7 +177,7 @@ Try extracting data from forms and documents using the Form Recognizer Studio. Y
 
 The layout model extracts text, selection marks, tables, paragraphs, and paragraph types (`roles`) from your documents.
 
-### Paragraph extraction <sup>🆕</sup>
+### Paragraph extraction
 
 The Layout model extracts all identified blocks of text in the `paragraphs` collection as a top level object under `analyzeResults`. Each entry in this collection represents a text block and includes the extracted text as`content`and the bounding `polygon` coordinates. The `span` information points to the text fragment within the top level `content` property that contains the full text from the document.
 
@@ -203,7 +191,7 @@ The Layout model extracts all identified blocks of text in the `paragraphs` coll
 ]
 ```
 
-### Paragraph roles<sup> 🆕</sup>
+### Paragraph roles
 
 The new machine-learning based page object detection extracts logical roles like titles, section headings, page headers, page footers, and more. The Form Recognizer Layout model assigns certain text blocks in the `paragraphs` collection with their specialized role or type predicted by the model. They're best used with unstructured documents to help understand the layout of the extracted content for a richer semantic analysis. The following paragraph roles are supported:
 
@@ -238,7 +226,7 @@ The new machine-learning based page object detection extracts logical roles like
 
 ### Pages extraction
 
-The pages collection is the very first object you see in the service response.
+The pages collection is the first object you see in the service response.
 
 ```json
 "pages": [
@@ -369,7 +357,7 @@ The second step is to call the [Get Analyze Layout Result](https://westcentralus
 
 |Field| Type | Possible values |
 |:-----|:----:|:----|
-|status | string | `notStarted`: The analysis operation has not started.<br /><br />`running`: The analysis operation is in progress.<br /><br />`failed`: The analysis operation has failed.<br /><br />`succeeded`: The analysis operation has succeeded.|
+|status | string | `notStarted`: The analysis operation hasn't started.<br /><br />`running`: The analysis operation is in progress.<br /><br />`failed`: The analysis operation has failed.<br /><br />`succeeded`: The analysis operation has succeeded.|
 
 Call this operation iteratively until it returns the `succeeded` value. Use an interval of 3 to 5 seconds to avoid exceeding the requests per second (RPS) rate.
 
@@ -399,7 +387,7 @@ Layout API extracts text from documents and images with multiple text angles and
 
 ### Tables with headers
 
-Layout API extracts tables in the `pageResults` section of the JSON output. Documents can be scanned, photographed, or digitized. Tables can be complex with merged cells or columns, with or without borders, and with odd angles. Extracted table information includes the number of columns and rows, row span, and column span. Each cell with its bounding box is output along with information whether it's recognized as part of a header or not. The model predicted header cells can span multiple rows and are not necessarily the first rows in a table. They also work with rotated tables. Each table cell also includes the full text with references to the individual words in the `readResults` section.
+Layout API extracts tables in the `pageResults` section of the JSON output. Documents can be scanned, photographed, or digitized. Tables can be complex with merged cells or columns, with or without borders, and with odd angles. Extracted table information includes the number of columns and rows, row span, and column span. Each cell with its bounding box is output along with information whether it's recognized as part of a header or not. The model predicted header cells can span multiple rows and aren't necessarily the first rows in a table. They also work with rotated tables. Each table cell also includes the full text with references to the individual words in the `readResults` section.
 
 ![Tables example](./media/layout-table-header-demo.gif)