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**This article applies to:****The latest [public preview SDK](sdk-preview.md) supported by Document Intelligence REST API version [2023-02-28-preview](https://westus.dev.cognitive.microsoft.com/docs/services/form-recognizer-api-2023-02-28-preview/operations/AnalyzeDocument)**.
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[!INCLUDE [applies to v3.1](includes/applies-to-v3-1.md)]
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> [!NOTE]
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> Add-on capabilities for Document Intelligence Studio are only available within the Read and Layout models for the `2023-02-28-preview`release.
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> Add-on capabilities for Document Intelligence Studio are available with the Read and Layout models for the `2023-07-31` (GA)release.
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Document Intelligence now supports more sophisticated analysis capabilities. These optional capabilities can be enabled and disabled depending on the scenario of the document extraction. There are three add-on capabilities available for the `2023-02-28-preview`:
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Document Intelligence now supports more sophisticated analysis capabilities. These optional capabilities can be enabled and disabled depending on the scenario of the document extraction. There are three add-on capabilities available for the `2023-07-31` (GA) release:
Copy file name to clipboardExpand all lines: articles/ai-services/document-intelligence/concept-analyze-document-response.md
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ms.date: 07/18/2023
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ms.author: vikurpad
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ms.custom: references_regions
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monikerRange: 'doc-intel-3.0.0'
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monikerRange: '>=doc-intel-3.0.0'
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---
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# Analyze document API response
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[!INCLUDE [applies to v3.1 and v3.0](includes/applies-to-v3-1-v3-0.md)]
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In this article, let's examine the different objects returned as part of the analyze document response and how to use the document analysis API response in your applications.
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ms.topic: conceptual
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ms.date: 07/18/2023
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ms.author: lajanuar
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monikerRange: '<=doc-intel-3.0.0'
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monikerRange: '<=doc-intel-3.1.0'
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---
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<!-- markdownlint-disable MD033 -->
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# Document Intelligence business card model
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::: moniker range="doc-intel-3.0.0"
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[!INCLUDE [applies to v3.0](includes/applies-to-v3-0.md)]
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::: moniker range=">=doc-intel-3.0.0"
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[!INCLUDE [applies to v3.1 and v3.0](includes/applies-to-v3-1-v3-0.md)]
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::: moniker-end
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::: moniker range="doc-intel-2.1.0"
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Business cards are a great way to represent a business or a professional. The company logo, fonts and background images found in business cards help promote 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 integration into for the benefit of their users.
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::: moniker range="doc-intel-3.0.0"
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::: moniker range=">=doc-intel-3.0.0"
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***Sample business card processed with [Document Intelligence 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="Screenshot of a sample business card analyzed in the Document Intelligence Studio." lightbox="./media/overview-business-card.jpg":::
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## Development options
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Document Intelligence v3.0 supports the following tools:
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# Document Intelligence composed custom models
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::: moniker range="doc-intel-3.0.0"
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[!INCLUDE [applies to v3.0](includes/applies-to-v3-0.md)]
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::: moniker range=">=doc-intel-3.0.0"
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[!INCLUDE [applies to v3.1 and v3.0](includes/applies-to-v3-1-v3-0.md)]
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::: moniker-end
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::: moniker-end
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With the introduction of [**custom classification models**](./concept-custom-classifier.md), you can choose to use a [**composed model**](./concept-composed-models.md) or [**classification model**](concept-custom-classifier.md) as an explicit step before analysis. For a deeper understanding of when to use a classification or composed model, _see_[**Custom classification models**](concept-custom-classifier.md#compare-custom-classification-and-composed-models).
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## Development options
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The following resources are supported by Document Intelligence **v3.0** :
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# Document Intelligence custom classification model
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**This article applies to:****The latest [public preview SDK](sdk-preview.md) supported by Document Intelligence REST API version [2023-02-28-preview](https://westus.dev.cognitive.microsoft.com/docs/services/form-recognizer-api-2023-02-28-preview/operations/AnalyzeDocument)**.
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> [!IMPORTANT]
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## Training a model
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Custom classification models are only available in the [v3.0 API](v3-migration-guide.md) starting with API version ```2023-02-28-preview```. [Document Intelligence Studio](https://formrecognizer.appliedai.azure.com/studio) provides a no-code user interface to interactively train a custom classifier.
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Custom classification models are available with the [v3.1 and v3.0 APIs](v3-migration-guide.md) starting with API version ```2023-07-31```. [Document Intelligence Studio](https://formrecognizer.appliedai.azure.com/studio) provides a no-code user interface to interactively train a custom classifier.
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When using the REST API, if you've organized your documents by folders, you can use the ```azureBlobSource``` property of the request to train a classification model.
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# Tips for building labeled datasets
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[!INCLUDE [applies to v3.1, v3.0, and v2.1](includes/applies-to-v3-1-v3-0-v2-1.md)]
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This article highlights the best methods for labeling custom model datasets in the Document Intelligence Studio. Labeling documents can be time consuming when you have a large number of labels, long documents, or documents with varying structure. These tips should help you label documents more efficiently.
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