Merge pull request #215945 from sanjeev3/main

SEO updates to Form Recognizer Docs (Overviews & Concepts)

Author: BCS2022

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

@@ -1,7 +1,7 @@
 ---
-title: Form Recognizer business card model
+title: Business card data extraction - Form Recognizer
 titleSuffix: Azure Applied AI Services
-description: Concepts related to data extraction and analysis using the prebuilt business card model.
+description: OCR and machine learning based business card scanning in Form Recognizer extracts key data from business cards.
 author: laujan
 manager: nitinme
 ms.service: applied-ai-services
@@ -14,10 +14,16 @@ recommendations: false
 ---
 <!-- markdownlint-disable MD033 -->
 
-# Form Recognizer business card model
+# Business card data extraction
 
 [!INCLUDE [applies to v3.0 and v2.1](includes/applies-to-v3-0-and-v2-1.md)]
 
+## How business card data extraction works
+
+Business cards are a great way of representing a business or a professional. The company logo, fonts and background images found in business cards help the company branding and differentiate it from others. Applying OCR and machine-learning based techniques to automate scanning of business cards is a common image processing scenario. Enterprise systems used by sales and marketing teams typically have business card data extraction capability integrated into them for the benefit of their users.
+
+## Form Recognizer Business Card model
+
 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)***
@@ -38,7 +44,7 @@ The following tools are supported by Form Recognizer v2.1:
 |----------|-------------------------|
 |**Business card model**|  <ul><li>[**Form Recognizer labeling tool**](https://fott-2-1.azurewebsites.net/prebuilts-analyze)</li><li>[**REST API**](/azure/applied-ai-services/form-recognizer/how-to-guides/use-sdk-rest-api?view=form-recog-2.1.0&preserve-view=true&tabs=windows&pivots=programming-language-rest-api#analyze-business-cards)</li><li>[**Client-library SDK**](/azure/applied-ai-services/form-recognizer/how-to-guides/v2-1-sdk-rest-api)</li><li>[**Form Recognizer Docker container**](containers/form-recognizer-container-install-run.md?tabs=business-card#run-the-container-with-the-docker-compose-up-command)</li></ul>|
 
-### Try Form Recognizer
+### Try business card data extraction
 
 See how data, including name, job title, address, email, and company name, is extracted from business cards using the Form Recognizer Studio or our Sample Labeling tool. You'll need the following resources:
 

articles/applied-ai-services/form-recognizer/concept-composed-models.md

@@ -1,7 +1,7 @@
 ---
-title: Form Recognizer composed models
+title: Composed custom models - Form Recognizer
 titleSuffix: Azure Applied AI Services
-description: Learn about composed custom models
+description: Compose several custom models into a single model for easier data extraction from groups of distinct form types.
 author: laujan
 manager: nitinme
 ms.service: applied-ai-services

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

@@ -1,7 +1,7 @@
 ---
-title: Form Recognizer custom neural model
+title: Custom neural document model - Form Recognizer
 titleSuffix: Azure Applied AI Services
-description: Learn about custom neural (neural) model type, its features and how you train a model with high accuracy to extract data from structured and unstructured documents.
+description: Use the custom neural document model to train a model to extract data from structured, semistructured, and unstructured documents.
 author: laujan
 manager: nitinme
 ms.service: applied-ai-services
@@ -14,12 +14,12 @@ monikerRange: 'form-recog-3.0.0'
 recommendations: false
 ---
 
-# Form Recognizer custom neural model
+# Custom neural document 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:
+Custom neural document models or neural models are a deep learned model type 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

@@ -1,7 +1,7 @@
 ---
-title: Form Recognizer custom template model
+title: Custom template document model - Form Recognizer
 titleSuffix: Azure Applied AI Services
-description: Learn about the custom template model type, its features and how you train a model with high accuracy to extract data from structured or templated forms
+description: Use the custom template document model to train a model to extract data from structured or templated forms.
 author: laujan
 manager: nitinme
 ms.service: applied-ai-services
@@ -13,11 +13,11 @@ monikerRange: 'form-recog-3.0.0'
 recommendations: false
 ---
 
-# Form Recognizer custom template model
+# Custom template document 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 (formerly custom form) is an easy-to-train document 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

@@ -1,7 +1,7 @@
 ---
-title: Form Recognizer custom and composed models
+title: Custom document models - Form Recognizer
 titleSuffix: Azure Applied AI Services
-description: Learn to create, use, and manage Form Recognizer custom and composed models.
+description: Label and train customized models for your documents and compose multiple models into a single model identifier.
 author: laujan
 manager: nitinme
 ms.service: applied-ai-services
@@ -12,17 +12,17 @@ ms.author: lajanuar
 monikerRange: '>=form-recog-2.1.0'
 recommendations: false
 ---
-# Form Recognizer custom models
+# Custom document 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.
 
-## Custom model types
+## Custom document model types
 
-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 document 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 (v3.0)
 
@@ -84,7 +84,7 @@ The following tools are supported by Form Recognizer v2.1:
 |Custom model| <ul><li>[Form Recognizer labeling tool](https://fott-2-1.azurewebsites.net)</li><li>[REST API](/azure/applied-ai-services/form-recognizer/how-to-guides/use-sdk-rest-api?view=form-recog-2.1.0&preserve-view=true&tabs=windows&pivots=programming-language-rest-api#analyze-forms-with-a-custom-model)</li><li>[Client library SDK](/azure/applied-ai-services/form-recognizer/how-to-guides/v2-1-sdk-rest-api)</li><li>[Form Recognizer Docker container](containers/form-recognizer-container-install-run.md?tabs=custom#run-the-container-with-the-docker-compose-up-command)</li></ul>|***custom-model-id***|
 
 
-### Try Form Recognizer
+### Try building a custom model
 
 Try extracting data from your specific or unique documents using custom models. You need the following resources:
 
@@ -113,7 +113,7 @@ Try extracting data from your specific or unique documents using custom models.
     > [!div class="nextstepaction"]
     > [Try Form Recognizer Studio](https://formrecognizer.appliedai.azure.com/studio/customform/projects)
 
-## Model capabilities
+## Custom model extraction summary
 
 This table compares the supported data extraction areas: