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[!INCLUDE [applies to v3.0 and v2.1](includes/applies-to-v3-0-and-v2-1.md)]
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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.
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## How business card data extraction works
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::: moniker range="form-recog-3.0.0"
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[!INCLUDE [applies to v3.0](includes/applies-to-v3-0.md)]
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::: moniker-end
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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.
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::: moniker range="form-recog-2.1.0"
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[!INCLUDE [applies to v2.1](includes/applies-to-v2-1.md)]
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::: moniker-end
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## Form Recognizer Business Card model
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## Business card data extraction works
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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.
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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.
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::: moniker range="form-recog-3.0.0"
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***Sample business card processed with [Form Recognizer Studio](https://formrecognizer.appliedai.azure.com/studio/prebuilt?formType=businessCard)***
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:::image type="content" source="./media/studio/overview-business-card-studio.png" alt-text="sample business card" lightbox="./media/overview-business-card.jpg":::
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:::image type="content" source="media/studio/overview-business-card-studio.png" alt-text="sample business card" lightbox="./media/overview-business-card.jpg":::
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::: moniker-end
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::: moniker range="form-recog-2.1.0"
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***Sample business processed with [Form Recognizer sample labeling tool](https://fott-2-1.azurewebsites.net/)***
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:::image type="content" source="media/business-card-example.jpg" alt-text="sample business card":::
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::: moniker-end
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## Development options
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::: moniker range="form-recog-3.0.0"
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The following tools are supported by Form Recognizer v3.0:
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:
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:::image type="content" source="media/containers/keys-and-endpoint.png" alt-text="Screenshot: keys and endpoint location in the Azure portal.":::
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::: moniker range="form-recog-3.0.0"
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#### Form Recognizer Studio
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> [!NOTE]
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> [!div class="nextstepaction"]
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> [Try Form Recognizer Studio](https://formrecognizer.appliedai.azure.com/studio/prebuilt?formType=businessCard)
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## Input requirements
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::: moniker-end
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::: moniker range="form-recog-2.1.0"
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## Form Recognizer sample labeling tool
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1. Navigate to the [Form Recognizer Sample Tool](https://fott-2-1.azurewebsites.net/).
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1. On the sample tool home page, select **Use prebuilt model to get data**.
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:::image type="content" source="media/label-tool/prebuilt-1.jpg" alt-text="Analyze results of Form Recognizer Layout":::
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1. Select the **Form Type** to analyze from the dropdown window.
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1. Choose a URL for the file you would like to analyze from the below options:
*[**Sample ID document**](https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/DriverLicense.png).
*[**Sample business card image**](https://raw.githubusercontent.com/Azure/azure-sdk-for-python/master/sdk/formrecognizer/azure-ai-formrecognizer/samples/sample_forms/business_cards/business-card-english.jpg).
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1. In the **Source** field, select **URL** from the dropdown menu, paste the selected URL, and select the **Fetch** button.
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* For best results, provide one clear photo or high-quality scan per document.
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* Supported file formats: JPEG/JPG, 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.
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* For PDF and TIFF, up to 2000 pages can be processed (with a free tier subscription, only the first two pages are processed).
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* The file size must be less than 500 MB for paid (S0) tier and 4 MB for free (F0) tier.
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* Image dimensions must be between 50 x 50 pixels and 10,000 x 10,000 pixels.
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* PDF dimensions are up to 17 x 17 inches, corresponding to Legal or A3 paper size, or smaller.
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* The total size of the training data is 500 pages or less.
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* If your PDFs are password-locked, you must remove the lock before submission.
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:::image type="content" source="media/label-tool/fott-select-url.png" alt-text="Screenshot of source location dropdown menu.":::
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1. In the **Form recognizer service endpoint** field, paste the endpoint that you obtained with your Form Recognizer subscription.
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1. In the **key** field, paste the key you obtained from your Form Recognizer resource.
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:::image type="content" source="media/fott-select-form-type.png" alt-text="Screenshot: select form type dropdown window.":::
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1. Select **Run analysis**. The Form Recognizer Sample Labeling tool will call the Analyze Prebuilt API and analyze the document.
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1. View the results - see the key-value pairs extracted, line items, highlighted text extracted and tables detected.
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:::image type="content" source="media/invoice-example-new.jpg" alt-text="Analyze Results of Form Recognizer invoice model":::
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> [!NOTE]
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> 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.
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| WorkPhones | Array of phone numbers | Work phone number(s) from business card | +1 xxx xxx xxxx |
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| OtherPhones | Array of phone numbers | Other phone number(s) from business card | +1 xxx xxx xxxx |
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## Form Recognizer v3.0
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::: moniker-end
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::: moniker range="form-recog-2.1.0"
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Form Recognizer v3.0 introduces several new features and capabilities.
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### Fields extracted
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|Name| Type | Description | Text |
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|:-----|:----|:----|:----|
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| ContactNames | array of objects | Contact name extracted from business card |[{ "FirstName": "John", "LastName": "Doe" }]|
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| FirstName | string | First (given) name of contact | "John" |
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| LastName | string | Last (family) name of contact | "Doe" |
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| CompanyNames | array of strings | Company name extracted from business card |["Contoso"]|
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| Departments | array of strings | Department or organization of contact |["R&D"]|
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| JobTitles | array of strings | Listed Job title of contact |["Software Engineer"]|
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| Emails | array of strings | Contact email extracted from business card |["[email protected]"]|
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| Websites | array of strings | Website extracted from business card |["https://www.contoso.com"]|
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| Addresses | array of strings | Address extracted from business card |["123 Main Street, Redmond, WA 98052"]|
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| MobilePhones | array of phone numbers | Mobile phone number extracted from business card |["+19876543210"]|
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| Faxes | array of phone numbers | Fax phone number extracted from business card |["+19876543211"]|
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| WorkPhones | array of phone numbers | Work phone number extracted from business card |["+19876543231"]|
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| OtherPhones | array of phone numbers | Other phone number extracted from business card |["+19876543233"]|
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## Supported locales
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**Pre-built business cards v2.1** supports the following locales:
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***en-us**
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***en-au**
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***en-ca**
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***en-gb**
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***en-in**
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### Migration guide and REST API v3.0
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* Follow our [**Form Recognizer v3.0 migration guide**](v3-migration-guide.md) to learn how to use the v3.0 version in your applications and workflows.
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* Explore our [**REST API**](https://westus.dev.cognitive.microsoft.com/docs/services/form-recognizer-api-2022-08-31/operations/AnalyzeDocument) to learn more about the v3.0 version and new capabilities.
*[Learn how to process your own forms and documents](quickstarts/try-v3-form-recognizer-studio.md) with the [Form Recognizer Studio](https://formrecognizer.appliedai.azure.com/studio)
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* Explore our REST API:
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* Complete a [Form Recognizer quickstart](quickstarts/get-started-sdks-rest-api.md?view=form-recog-3.0.0&preserve-view=true) and get started creating a document processing app in the development language of your choice.
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::: moniker-end
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::: moniker range="form-recog-2.1.0"
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*[Learn how to process your own forms and documents](quickstarts/try-sample-label-tool.md) with the [Form Recognizer sample labeling tool](https://fott-2-1.azurewebsites.net/)
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* Complete a [Form Recognizer quickstart](quickstarts/get-started-sdks-rest-api.md?view=form-recog-2.1.0&preserve-view=true) and get started creating a document processing app in the development language of your choice.
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::: moniker-end
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> [!div class="nextstepaction"]
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> [Form Recognizer API v3.0](https://westus.dev.cognitive.microsoft.com/docs/services/form-recognizer-api-2022-08-31/operations/AnalyzeDocument)
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).
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### Key-value extraction
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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.
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ms.service: applied-ai-services
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ms.subservice: forms-recognizer
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ms.topic: conceptual
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ms.date: 10/27/2022
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ms.date: 11/10/2022
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ms.author: lajanuar
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recommendations: false
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ms.custom: references.regions
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---
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<!-- markdownlint-disable MD033 -->
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# Identity document (ID) processing
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# Azure Form Recognizer Identity document (ID) model
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The Form Recognizer Identity document (ID) model combines Optical Character Recognition (OCR) with deep learning models to analyze and extract key information from identity documents: US Drivers Licenses (all 50 states and District of Columbia), international passport biographical pages, US state IDs, social security cards, and permanent resident cards and more. The API analyzes identity documents, extracts key information, and returns a structured JSON data representation.
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::: moniker range="form-recog-3.0.0"
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[!INCLUDE [applies to v3.0](includes/applies-to-v3-0.md)]
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[!INCLUDE [applies to v2.1](includes/applies-to-v2-1.md)]
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::: moniker-end
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## What is identity document (ID) processing
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## Identity document (ID) processing
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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.
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::: moniker range="form-recog-3.0.0"
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## Form Recognizer Identity document (ID) model
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The Form Recognizer Identity document (ID) model combines Optical Character Recognition (OCR) with deep learning models to analyze and extract key information from identity documents: US Drivers Licenses (all 50 states and District of Columbia), international passport biographical pages, US state IDs, social security cards, and permanent resident cards and more. The API analyzes identity documents, extracts key information, and returns a structured JSON data representation.
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***Sample U.S. Driver's License processed with [Form Recognizer Studio](https://formrecognizer.appliedai.azure.com/studio/prebuilt?formType=idDocument)***
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:::image type="content" source="media/studio/analyze-drivers-license.png" alt-text="Image of a sample driver's license.":::
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ms.service: applied-ai-services
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ms.topic: conceptual
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ms.date: 11/08/2022
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ms.date: 11/10/2022
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ms.author: lajanuar
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---
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# Document layout analysis
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# Azure Form Recognizer layout model
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The Form Recognizer Layout is an advanced machine-learning based document layout analysis model available in the Form Recognizer cloud API. In the version v2.1, the document layout model extracted text lines, words, tables, and selection marks.
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::: moniker range="form-recog-3.0.0"
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[!INCLUDE [applies to v3.0](includes/applies-to-v3-0.md)]
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[!INCLUDE [applies to v2.1](includes/applies-to-v2-1.md)]
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::: moniker-end
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## What is document layout analysis?
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## Document layout analysis
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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.
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The following illustration shows the typical components in an image of a sample page.
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:::image type="content" source="media/document-layout-example.png" alt-text="Illustration of document layout example.":::
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## Form Recognizer Layout model
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The Form Recognizer Layout is an advanced machine-learning based document layout analysis model available in the Form Recognizer cloud API. In the version v2.1, the document layout model extracted text lines, words, tables, and selection marks.
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::: moniker range="form-recog-3.0.0"
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***Sample form processed with [Form Recognizer Studio](https://formrecognizer.appliedai.azure.com/studio/layout)***
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:::image type="content" source="media/studio/form-recognizer-studio-layout-newspaper.png" alt-text="Screenshot of sample newspaper page processed using Form Recognizer studio":::
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