Merge pull request #191464 from laujan/edit-pr-190696

Edit pr 190696

Author: PRMerger4

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

@@ -7,7 +7,7 @@ manager: nitinme
 ms.service: applied-ai-services
 ms.subservice: forms-recognizer
 ms.topic: conceptual
-ms.date: 11/02/2021
+ms.date: 03/11/2022
 ms.author: lajanuar
 recommendations: false
 ms.custom: ignite-fall-2021
@@ -18,7 +18,7 @@ ms.custom: ignite-fall-2021
 
 The business card model combines powerful Optical Character Recognition (OCR) capabilities with deep learning models to analyze and extract key information from business card images. The API analyzes printed business cards; extracts key information such as first name, last name, company name, email address, and phone number;  and returns a structured JSON data representation.
 
-***Sample business card processed with Form Recognizer Studio***
+***Sample business card processed with [Form Recognizer Studio](https://formrecognizer.appliedai.azure.com/studio/prebuilt?formType=businessCard)***
 
 :::image type="content" source="./media/studio/overview-business-card-studio.png" alt-text="sample business card" lightbox="./media/overview-business-card.jpg":::
 
@@ -64,7 +64,7 @@ See how data, including name, job title, address, email, and company name, is ex
 
 #### Sample Labeling tool
 
-You will need a business card document. You can use our [sample business card document](https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/businessCard.png).
+You'll need a business card document. You can use our [sample business card document](https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/businessCard.png).
 
   1. On the Sample Labeling tool home page, select **Use prebuilt model to get data**.
 
@@ -81,7 +81,7 @@ You will need a business card document. You can use our [sample business card do
 * Supported file formats: JPEG, PNG, BMP, TIFF, and PDF (text-embedded or scanned). Text-embedded PDFs are best to eliminate the possibility of error in character extraction and location.
 * For PDF and TIFF, up to 2000 pages can be processed (with a free tier subscription, only the first two pages are processed).
 * The file size must be less than 50 MB.
-* Image dimensions must be between 50 x 50 pixels and 10000 x 10000 pixels.
+* Image dimensions must be between 50 x 50 pixels and 10,000 x 10,000 pixels.
 * PDF dimensions are up to 17 x 17 inches, corresponding to Legal or A3 paper size, or smaller.
 * The total size of the training data is 500 pages or less.
 * If your PDFs are password-locked, you must remove the lock before submission.

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

@@ -7,7 +7,7 @@ manager: nitinme
 ms.service: applied-ai-services
 ms.subservice: forms-recognizer
 ms.topic: conceptual
-ms.date: 11/02/2021
+ms.date: 03/11/2022
 ms.author: lajanuar
 recommendations: false
 ms.custom: ignite-fall-2021
@@ -18,7 +18,7 @@ ms.custom: ignite-fall-2021
 
 The ID document model combines Optical Character Recognition (OCR) with deep learning models to analyze and extracts key information from US Drivers Licenses (all 50 states and District of Columbia) and international passport biographical pages (excludes visa and other travel documents). The API analyzes identity documents, extracts key information, and returns a structured JSON data representation.
 
-***Sample U.S. Driver's License processed with Form Recognizer Studio***
+***Sample U.S. Driver's License processed with [Form Recognizer Studio](https://formrecognizer.appliedai.azure.com/studio/prebuilt?formType=idDocument)***
 
 :::image type="content" source="media/studio/analyze-drivers-license.png" alt-text="Image of a sample driver's license." lightbox="media/overview-id.jpg":::
 

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: 11/02/2021
+ms.date: 03/11/2022
 ms.author: lajanuar
 recommendations: false
 ms.custom: ignite-fall-2021
@@ -17,9 +17,9 @@ ms.custom: ignite-fall-2021
 
 The Form Recognizer Layout API extracts text, tables, selection marks, and structure information from documents (PDF, TIFF) and images (JPG, PNG, BMP).
 
-***Sample form processed with [Form Recognizer Sample Labeling tool](https://fott-2-1.azurewebsites.net/)  layout feature***
+***Sample form processed with [Form Recognizer Studio](https://formrecognizer.appliedai.azure.com/studio/layout)***
 
-:::image type="content" source="media/layout-demo.gif" alt-text="Screenshot: Sample Labeling tool processing gif.":::
+:::image type="content" source="media/studio/analyze-layout.png" alt-text="Screenshot: Screenshot of sample document processed using Form Recognizer studio":::
 
 **Data extraction features**
 
@@ -92,20 +92,20 @@ You'll need a form document. You can use our [sample form document](https://raw.
 * Supported file formats: JPEG, PNG, BMP, TIFF, and PDF (text-embedded or scanned). Text-embedded PDFs are best to eliminate the possibility of error in character extraction and location.
 * For PDF and TIFF, up to 2000 pages can be processed (with a free tier subscription, only the first two pages are processed).
 * The file size must be less than 50 MB.
-* Image dimensions must be between 50 x 50 pixels and 10000 x 10000 pixels.
+* Image dimensions must be between 50 x 50 pixels and 10,000 x 10,000 pixels.
 
 > [!NOTE]
 > The [Sample Labeling tool](https://fott-2-1.azurewebsites.net/) does not support the BMP file format. This is a limitation of the tool not the Form Recognizer Service.
 
 ## Supported languages and locales
 
- Form Recognizer preview version introduces additional language support for the layout model. *See* our [Language Support](language-support.md) for a complete list of supported handwritten and printed languages.
+ Form Recognizer preview version introduces additional language support for the layout model. *See* [Language Support](language-support.md) for a complete list of supported handwritten and printed languages.
 
 ## Features
 
 ### Tables and table 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.
 
 :::image type="content" source="./media/layout-table-headers-example.png" alt-text="Layout table headers output":::
 
@@ -117,15 +117,15 @@ Layout API also extracts selection marks from documents. Extracted selection mar
 
 ### Text lines and words
 
-Layout 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). All the text information is included in the `readResults` section of the JSON output.
+Layout 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 in lines, words, and bounding boxes. All the text information is included in the `readResults` section of the JSON output.
 
 :::image type="content" source="./media/layout-text-extraction.png" alt-text="Layout text extraction output":::
 
 ### Natural reading order for text lines (Latin only)
 
 In Form Recognizer v2.1, you can specify the order in which the text lines are output with the `readingOrder` query parameter. Use `natural` for a more human-friendly reading order output as shown in the following example. This feature is only supported for Latin languages.
 
-In Form Recognizer v3.0, the natural reading order output is used by the service in all cases. Therefore, there is no `readingOrder` parameter provided in this version.
+In Form Recognizer v3.0, the natural reading order output is used by the service in all cases. Therefore, there's no `readingOrder` parameter provided in this version.
 
 ### Handwritten classification for text lines (Latin only)
 

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

@@ -7,7 +7,7 @@ manager: nitinme
 ms.service: applied-ai-services
 ms.subservice: forms-recognizer
 ms.topic: conceptual
-ms.date: 11/02/2021
+ms.date: 03/11/2022
 ms.author: lajanuar
 recommendations: false
 ms.custom: ignite-fall-2021
@@ -18,7 +18,7 @@ ms.custom: ignite-fall-2021
 
 The receipt model combines powerful Optical Character Recognition (OCR) capabilities with deep learning models to analyze and extract key information from sales receipts. Receipts can be of various formats and quality including printed and handwritten receipts. The API extracts key information such as merchant name, merchant phone number, transaction date, tax, and transaction total and returns a structured JSON data representation.
 
-***Sample receipt processed with [Form Recognizer Sample Labeling tool](https://fott-2-1.azurewebsites.net/)***:
+***Sample receipt processed with [Form Recognizer Studio](https://formrecognizer.appliedai.azure.com/studio/prebuilt?formType=receipt)***:
 
 :::image type="content" source="media/studio/overview-receipt.png" alt-text="sample receipt" lightbox="media/overview-receipt.jpg":::