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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:
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The task of recognizing small text from large-size documents, like engineering drawings, is a challenge. Often the text is mixed with other graphical elements and has varying fonts, sizes and orientations. Moreover, the text may be broken into separate parts or connected with other symbols. Document Intelligence now supports extracting content from these types of documents with the `ocr.highResolution` capability. You get improved quality of content extraction from A1/A2/A3 documents by enabling this add-on capability.
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## Barcode extraction
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The Read OCR model extracts all identified barcodes in the `barcodes` collection as a top level object under `content`. Inside the `content`, detected barcodes are represented as `:barcode:`. Each entry in this collection represents a barcode and includes the barcode type as `kind` and the embedded barcode content as `value` along with its `polygon` coordinates. Initially, barcodes appear at the end of each page. Here, the `confidence` is hard-coded for the public preview (`2023-02-28`) release.
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### Supported barcode types
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|**Barcode Type**|**Example**|
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| --- | --- |
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| QR Code |:::image type="content" source="media/barcodes/qr-code.png" alt-text="Screenshot of the QR Code.":::|
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| Code 39 |:::image type="content" source="media/barcodes/code-39.png" alt-text="Screenshot of the Code 39.":::|
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| Code 128 |:::image type="content" source="media/barcodes/code-128.png" alt-text="Screenshot of the Code 128.":::|
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| UPC (UPC-A & UPC-E) |:::image type="content" source="media/barcodes/upc.png" alt-text="Screenshot of the UPC.":::|
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| PDF417 |:::image type="content" source="media/barcodes/pdf-417.png" alt-text="Screenshot of the PDF417.":::|
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## Formula extraction
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The `ocr.formula` capability extracts all identified formulas, such as mathematical equations, in the `formulas` collection as a top level object under `content`. Inside `content`, detected formulas are represented as `:formula:`. Each entry in this collection represents a formula that includes the formula type as `inline` or `display`, and its LaTeX representation as `value` along with its `polygon` coordinates. Initially, formulas appear at the end of each page.
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> [!NOTE]
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> The `confidence` score is hard-coded for the `2023-02-28` public preview release.
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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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[!INCLUDE [applies to v3.0](includes/applies-to-v3-0.md)]
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[!INCLUDE [applies to v3.1 and v3.0](includes/applies-to-v3-1-v3-0.md)]
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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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