Merge pull request #214534 from laujan/update-versioning-presentation

add monikers

Author: Court72

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

@@ -7,11 +7,15 @@ manager: nitinme
 ms.service: applied-ai-services
 ms.subservice: forms-recognizer
 ms.topic: conceptual
-ms.date: 10/10/2022
+ms.date: 10/14/2022
 ms.author: lajanuar
+monikerRange: '>=form-recog-2.1.0'
+recommendations: false
 ---
 
-# Interpret and improve accuracy and confidence for custom models
+# Accuracy and confidence scores for custom models
+
+[!INCLUDE [applies to v3.0 and v2.1](includes/applies-to-v3-0-and-v2-1.md)]
 
 > [!NOTE]
 >

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

@@ -7,14 +7,17 @@ manager: nitinme
 ms.service: applied-ai-services
 ms.subservice: forms-recognizer
 ms.topic: conceptual
-ms.date: 10/10/2022
+ms.date: 10/14/2022
 ms.author: lajanuar
+monikerRange: '>=form-recog-2.1.0'
 recommendations: false
 ---
 <!-- markdownlint-disable MD033 -->
 
 # Form Recognizer business card model
 
+[!INCLUDE [applies to v3.0 and v2.1](includes/applies-to-v3-0-and-v2-1.md)]
+
 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](https://formrecognizer.appliedai.azure.com/studio/prebuilt?formType=businessCard)***

articles/applied-ai-services/form-recognizer/concept-custom-neural.md

@@ -10,11 +10,15 @@ ms.topic: conceptual
 ms.date: 10/10/2022
 ms.author: lajanuar
 ms.custom: references_regions
+monikerRange: 'form-recog-3.0.0'
 recommendations: false
 ---
 
 # Form Recognizer custom neural model
 
+**This article applies to:** ![Form Recognizer v3.0 checkmark](media/yes-icon.png) **Form Recognizer v3.0**.
+
+
 Custom neural models or neural models are a deep learned model that combines layout and language features to accurately extract labeled fields from documents. The base custom neural model is trained on various document types that makes it suitable to be trained for extracting fields from structured, semi-structured and unstructured documents. The table below lists common document types for each category:
 
 |Documents | Examples |

articles/applied-ai-services/form-recognizer/concept-custom-template.md

@@ -9,11 +9,14 @@ ms.subservice: forms-recognizer
 ms.topic: conceptual
 ms.date: 10/10/2022
 ms.author: lajanuar
+monikerRange: 'form-recog-3.0.0'
 recommendations: false
 ---
 
 # Form Recognizer custom template model
 
+**This article applies to:** ![Form Recognizer v3.0 checkmark](media/yes-icon.png) **Form Recognizer v3.0**.
+
 Custom template (formerly custom form) is an easy-to-train model that accurately extracts labeled key-value pairs, selection marks, tables, regions, and signatures from documents. Template models use layout cues to extract values from documents and are suitable to extract fields from highly structured documents with defined visual templates.
 
 Custom template models share the same labeling format and strategy as custom neural models, with support for more field types and languages.

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

@@ -9,10 +9,13 @@ ms.subservice: forms-recognizer
 ms.topic: conceptual
 ms.date: 08/22/2022
 ms.author: lajanuar
+monikerRange: '>=form-recog-2.1.0'
 recommendations: false
 ---
 # Form Recognizer custom models
 
+[!INCLUDE [applies to v3.0 and v2.1](includes/applies-to-v3-0-and-v2-1.md)]
+
 Form Recognizer uses advanced machine learning technology to detect and extract information from forms and documents and returns the extracted data in a structured JSON output. With Form Recognizer, you can use pre-built or pre-trained models or you can train standalone custom models. Custom models extract and analyze distinct data and use cases from forms and documents specific to your business. Standalone custom models can be combined to create [composed models](concept-composed-models.md).
 
 To create a custom model, you label a dataset of documents with the values you want extracted and train the model on the labeled dataset. You only need five examples of the same form or document type to get started.
@@ -21,7 +24,7 @@ To create a custom model, you label a dataset of documents with the values you w
 
 Custom models can be one of two types, [**custom template**](concept-custom-template.md ) or custom form and [**custom neural**](concept-custom-neural.md)  or custom document models. The labeling and training process for both models is identical, but the models differ as follows:
 
-### Custom template model
+### Custom template model (v3.0)
 
 The custom template or custom form model relies on a consistent visual template to extract the labeled data. The accuracy of your model is affected by variances in the visual structure of your documents. Structured  forms such as questionnaires or applications are examples of consistent visual templates.
 
@@ -33,7 +36,7 @@ Your training set will consist of structured documents where the formatting and
 >
 > For more information, *see* [Interpret and improve accuracy and confidence for custom models](concept-accuracy-confidence.md).
 
-### Custom neural model
+### Custom neural model (v3.0)
 
 The custom neural (custom document) model uses deep learning models and  base model trained on a large collection of documents. This model is then fine-tuned or adapted to your data when you train the model with a labeled dataset. Custom neural models support structured, semi-structured, and unstructured documents to extract fields. Custom neural models currently support English-language documents. When you're choosing between the two model types, start with a neural model to determine if it meets your functional needs. See [neural models](concept-custom-neural.md) to learn more about custom document models.
 

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

@@ -2,19 +2,21 @@
 title: "Form Recognizer Studio"
 titleSuffix: Azure Applied AI Services
 description: "Concept: Form and document processing, data extraction, and analysis using Form Recognizer Studio "
-author: sanjeev3
+author: laujan
 manager: netahw
 ms.service: applied-ai-services
 ms.subservice: forms-recognizer
 ms.topic: conceptual
-ms.date: 10/10/2022
+ms.date: 10/14/2022
 ms.author: sajagtap
 monikerRange: 'form-recog-3.0.0'
 recommendations: false
 ---
 
 # Form Recognizer Studio
 
+**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 pre-trained 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.

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

@@ -7,14 +7,16 @@ manager: nitinme
 ms.service: applied-ai-services
 ms.subservice: forms-recognizer
 ms.topic: conceptual
-ms.date: 10/10/2022
+ms.date: 10/14/2022
 ms.author: lajanuar
 monikerRange: 'form-recog-3.0.0'
 recommendations: false
 ---
 <!-- markdownlint-disable MD033 -->
 
-# Form Recognizer general document model 
+# Form Recognizer general document model
+
+**This article applies to:** ![Form Recognizer v3.0 checkmark](media/yes-icon.png) **Form Recognizer v3.0**.
 
 The General document v3.0 model combines powerful Optical Character Recognition (OCR) capabilities with deep learning models to extract key-value pairs, tables, and selection marks from documents. General document is only available with the v3.0 API.  For more information on using the v3.0 API, see our [migration guide](v3-migration-guide.md).
 

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

@@ -7,14 +7,17 @@ manager: nitinme
 ms.service: applied-ai-services
 ms.subservice: forms-recognizer
 ms.topic: conceptual
-ms.date: 10/10/2022
+ms.date: 10/14/2022
 ms.author: lajanuar
+monikerRange: '>=form-recog-2.1.0'
 recommendations: false
 ---
 <!-- markdownlint-disable MD033 -->
 
 # Form Recognizer ID document model
 
+[!INCLUDE [applies to v3.0 and v2.1](includes/applies-to-v3-0-and-v2-1.md)]
+
 The ID document model combines Optical Character Recognition (OCR) with deep learning models to analyze and extract key information from US Drivers Licenses (all 50 states and District of Columbia), international passport biographical pages, US state IDs, social security cards, and permanent resident (green) cards. 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](https://formrecognizer.appliedai.azure.com/studio/prebuilt?formType=idDocument)***

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

@@ -7,14 +7,17 @@ manager: nitinme
 ms.service: applied-ai-services
 ms.subservice: forms-recognizer
 ms.topic: conceptual
-ms.date: 10/10/2022
+ms.date: 10/14/2022
 ms.author: lajanuar
+monikerRange: '>=form-recog-2.1.0'
 recommendations: false
 ---
 <!-- markdownlint-disable MD033 -->
 
 # Form Recognizer invoice model
 
+[!INCLUDE [applies to v3.0 and v2.1](includes/applies-to-v3-0-and-v2-1.md)]
+
  The invoice model combines powerful Optical Character Recognition (OCR) capabilities with deep learning models to analyze and extract key fields and line items from sales invoices.  Invoices can be of various formats and quality including phone-captured images, scanned documents, and digital PDFs. The API analyzes invoice text; extracts key information such as customer name, billing address, due date, and amount due; and returns a structured JSON data representation. The model currently supports both English and Spanish invoices.
 
 **Sample invoice processed with [Form Recognizer Studio](https://formrecognizer.appliedai.azure.com/studio/prebuilt?formType=invoice)**:

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

@@ -7,13 +7,16 @@ manager: nitinme
 ms.service: applied-ai-services
 ms.subservice: forms-recognizer
 ms.topic: conceptual
-ms.date: 10/10/2022
+ms.date: 10/14/2022
 ms.author: lajanuar
+monikerRange: '>=form-recog-2.1.0'
 recommendations: false
 ---
 
 # Form Recognizer layout model
 
+[!INCLUDE [applies to v3.0 and v2.1](includes/applies-to-v3-0-and-v2-1.md)]
+
 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 Studio](https://formrecognizer.appliedai.azure.com/studio/layout)***
@@ -111,6 +114,7 @@ The Layout model extracts all identified blocks of text in the `paragraphs` coll
     }
 ]
 ```
+
 ### Paragraph roles<sup> 🆕</sup>
 
 The Layout model may flag certain paragraphs with their specialized type or `role` as 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:
@@ -143,6 +147,7 @@ The Layout model may flag certain paragraphs with their specialized type or `rol
 }
 
 ```
+
 ### Pages
 
 The pages collection is the very first object you see in the service response.
@@ -162,6 +167,7 @@ The pages collection is the very first object you see in the service response.
     }
 ]
 ```
+
 ### Text lines and words
 
 Read extracts print and handwritten style text as `lines` and `words`. The model outputs bounding `polygon` coordinates and `confidence` for the extracted words. The `styles` collection includes any handwritten style for lines if detected along with the spans pointing to the associated text. This feature applies to [supported handwritten languages](language-support.md).
@@ -202,6 +208,7 @@ Layout API also extracts selection marks from documents. Extracted selection mar
     ]
 }
 ```
+
 ### Tables and table headers
 
 Layout API extracts tables in the `pageResults` section of the JSON output. Documents can be scanned, photographed, or digitized. Extracted table information includes the number of columns and rows, row span, and column span. Each cell with its bounding `polygon` is output along with information whether it's recognized as a `columnHeader` or not. The API also works with rotated tables. Each table cell contains the row and column index and bounding polygon coordinates. For the cell text, the model outputs the `span` information containing the starting index (`offset`). The model also outputs the `length` within the top level `content` that contains the full text from the document.
@@ -228,6 +235,7 @@ Layout API extracts tables in the `pageResults` section of the JSON output. Docu
 }
 
 ```
+
 ### Select page numbers or ranges for text extraction
 
 For large multi-page documents, use the `pages` query parameter to indicate specific page numbers or page ranges for text extraction.