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articles/ai-services/document-intelligence/concept-composed-models.md

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|**Custom Neural models v3.0**|Not Supported|Not Supported|Supported|Supported|
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|**Custom Neural models v3.1**|Not Supported|Not Supported|Supported|Supported|
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* To compose a model trained with a prior version of the API (v2.1 or earlier), train a model with the v3.0 API using the same labeled dataset. That addition ensures that the v2.1 model can be composed with other models.
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* Models composed with v2.1 of the API continues to be supported, requiring no updates.
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* With models composed using v2.1 of the API continues to be supported, requiring no updates.
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* The limit for maximum number of custom models that can be composed is 100.
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* For custom models, the maximum number that can be composed is 100.
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::: moniker-end
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articles/ai-services/document-intelligence/concept-contract.md

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ms.custom:
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- ignite-2023
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ms.topic: conceptual
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ms.date: 11/21/2023
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ms.date: 03/06/2024
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ms.author: lajanuar
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monikerRange: '>=doc-intel-3.0.0'
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---
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See how data, including customer information, vendor details, and line items, is extracted from contracts. You need the following resources:
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* An Azure subscription—you can [create one for free](https://azure.microsoft.com/free/cognitive-services/)
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* An Azure subscription—you can [create one for free](https://azure.microsoft.com/free/cognitive-services/).
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* A [Document Intelligence instance](https://portal.azure.com/#create/Microsoft.CognitiveServicesFormRecognizer) in the Azure portal. You can use the free pricing tier (`F0`) to try the service. After your resource deploys, select **Go to resource** to get your key and endpoint.
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:::image type="content" source="media/containers/keys-and-endpoint.png" alt-text="Screenshot of keys and endpoint location in the Azure portal.":::
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## Document Intelligence Studio
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1. On the Document Intelligence Studio home page, select **Tax Documents**
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1. On the Document Intelligence Studio home page, select **Tax Documents**.
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1. You can analyze the sample tax documents or upload your own files.
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1. Select the **Run analysis** button and, if necessary, configure the **Analyze options** :
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1. Select the **Run analysis** button and, if necessary, configure the **Analyze options**:
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:::image type="content" source="media/studio/run-analysis-analyze-options.png" alt-text="Screenshot of Run analysis and Analyze options buttons in the Document Intelligence Studio.":::
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## Next steps
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* Try processing your own forms and documents with the [Document Intelligence Studio](https://formrecognizer.appliedai.azure.com/studio)
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* Try processing your own forms and documents with the [Document Intelligence Studio](https://formrecognizer.appliedai.azure.com/studio).
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* Complete a [Document Intelligence quickstart](quickstarts/get-started-sdks-rest-api.md?view=doc-intel-3.0.0&preserve-view=true) and get started creating a document processing app in the development language of your choice.

articles/ai-services/document-intelligence/concept-custom-lifecycle.md

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With the v3.1 API, custom models introduce a new model expiration property. The model expiration is set to two years from the date the model is built for all requests that use a GA API to build a model. To continue to use the model past the expiration date, you need to train the model with a current GA API version. The API version can be the one that the model was originally trained with or a later API version. The following figure illustrates the options when you need to retrain an expiring or expired model.
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[ ![Screenshot showing how to chose an API version to re-train a model.](media/model-lifecycle.png) ](media/model-lifecycle.png)
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:::image type="content" source="media/model-lifecycle.png" alt-text="Screenshot showing how to chose an API version to re-train a model.":::
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## Models trained with preview API version
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articles/ai-services/document-intelligence/concept-custom-template.md

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| Model | REST API | SDK | Label and Test Models|
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|--|--|--|--|
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| Custom template | [v3.1 API](/rest/api/aiservices/document-models/build-model?view=rest-aiservices-2024-02-29-preview&branch=docintelligence&tabs=HTTP)| [Document Intelligence SDK](quickstarts/get-started-sdks-rest-api.md?view=doc-intel-3.1.0&preserve-view=true)| [Document Intelligence Studio](https://formrecognizer.appliedai.azure.com/studio)|
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| Custom template | [v3.1 API](/rest/api/aiservices/document-models/build-model?view=rest-aiservices-2024-02-29-preview&preserve-view=true&branch=docintelligence&tabs=HTTP)| [Document Intelligence SDK](quickstarts/get-started-sdks-rest-api.md?view=doc-intel-3.1.0&preserve-view=true)| [Document Intelligence Studio](https://formrecognizer.appliedai.azure.com/studio)|
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With the v3.0 and later APIs, the build operation to train model supports a new ```buildMode``` property, to train a custom template model, set the ```buildMode``` to ```template```.
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articles/ai-services/document-intelligence/concept-general-document.md

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---
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title: General key-value extraction - Document Intelligence (formerly Form Recognizer)
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titleSuffix: Azure AI services
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description: Extract key-value pairs, tables, selection marks, and text from your documents with Document Intelligence
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description: Extract key-value pairs, tables, selection marks, and text from your documents with Document Intelligence.
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author: laujan
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manager: nitinme
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ms.service: azure-ai-document-intelligence
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ms.topic: conceptual
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ms.date: 03/06/2024
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---
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:::moniker range="doc-intel-4.0.0"
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> [!IMPORTANT]
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> Starting with Document Intelligence **2024-02-29-preview, 2023-10-31-preview** and going forward, the general document model (prebuilt-document) is deprecated. To extract key-value pairs, selection marks, text, tables, and structure from documents, use the following models:
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> Starting with `Document Intelligence versions **2024-02-29-preview, 2023-10-31-preview** and going forward, the general document model (prebuilt-document) is deprecated. To extract key-value pairs, selection marks, text, tables, and structure from documents, use the following models:
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| Feature | version| Model ID |
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|---------- |---------|--------|
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**This content applies to:** ![checkmark](media/yes-icon.png) **v3.0 (GA)** | **Latest versions:** ![purple-checkmark](media/purple-yes-icon.png) [**v4.0 (preview)**](?view=doc-intel-4.0.0&preserve-view=true) ![purple-checkmark](media/purple-yes-icon.png) [**v3.1 (preview)**](?view=doc-intel-3.1.0&preserve-view=true)
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The General document 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 available with the v3.1 and v3.0 APIs. For more information, _see_ our [migration guide](v3-1-migration-guide.md).
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The General document 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 available with the v3.1 and v3.0 APIs. For more information, _see_ our [migration guide](v3-1-migration-guide.md).
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::: moniker range="doc-intel-3.1.0 || doc-intel-3.0.0"
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* The general document model supports structured, semi-structured, and unstructured documents.
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* Selection marks are identified as fields with a value of ```:selected:``` or ```:unselected:```
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* Selection marks are identified as fields with a value of `:selected:` or `:unselected:`.
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***Sample document processed in the Document Intelligence Studio***
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You need the following resources:
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* An Azure subscription—you can [create one for free](https://azure.microsoft.com/free/cognitive-services/)
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* An Azure subscription—you can [create one for free](https://azure.microsoft.com/free/cognitive-services/).
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* A [Document Intelligence instance](https://portal.azure.com/#create/Microsoft.CognitiveServicesFormRecognizer) in the Azure portal. You can use the free pricing tier (`F0`) to try the service. After your resource deploys, select **Go to resource** to get your key and endpoint.
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> [!NOTE]
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> Document Intelligence Studio and the general document model are available with the v3.0 API.
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1. On the Document Intelligence Studio home page, select **General documents**.
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1. You can analyze the sample document or upload your own files.
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1. Select the **Run analysis** button and, if necessary, configure the **Analyze options** :
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1. Select the **Run analysis** button and, if necessary, configure the **Analyze options**:
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:::image type="content" source="media/studio/run-analysis-analyze-options.png" alt-text="Screenshot of Run analysis and Analyze options buttons in the Document Intelligence Studio.":::
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> [!div class="nextstepaction"]
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> [Try Document Intelligence Studio](https://formrecognizer.appliedai.azure.com/studio/prebuilt?formType=document)
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> [Try Document Intelligence Studio](https://formrecognizer.appliedai.azure.com/studio/prebuilt?formType=document).
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## Key-value pairs
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Key-value pairs are specific spans within the document that identify a label or key and its associated response or value. In a structured form, these pairs could be the label and the value the user entered for that field. In an unstructured document, they could be the date a contract was executed on based on the text in a paragraph. The AI model is trained to extract identifiable keys and values based on a wide variety of document types, formats, and structures.
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Key-value pairs are specific spans within the document that identify a label or key and its associated response or value. In a structured form, these pairs could be the label and the value the user entered for that field. In an unstructured document, they could be the date a contract was executed on based on the text in a paragraph. The AI model is trained to extract identifiable keys and values based on a wide variety of document types, formats, and structures.
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Keys can also exist in isolation when the model detects that a key exists, with no associated value or when processing optional fields. For example, a middle name field can be left blank on a form in some instances. Key-value pairs are spans of text contained in the document. For documents where the same value is described in different ways, for example, customer/user, the associated key is either customer or user (based on context).
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## Considerations
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* Because keys are spans of text extracted from the document, for semi structured documents, keys need to be mapped to an existing dictionary of keys.
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* Expect to see key-value pairs with a key, but no value. For example if a user chose to not provide an email address on the form.
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articles/ai-services/document-intelligence/concept-health-insurance-card.md

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**This content applies to:** ![checkmark](media/yes-icon.png) **v3.0 (GA)** | **Latest versions:** ![purple-checkmark](media/purple-yes-icon.png) [**v4.0 (preview)**](?view=doc-intel-4.0.0&preserve-view=true) ![purple-checkmark](media/purple-yes-icon.png) [**v3.1 (preview)**](?view=doc-intel-3.1.0&preserve-view=true)
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The Document Intelligence health insurance card model combines powerful Optical Character Recognition (OCR) capabilities with deep learning models to analyze and extract key information from US health insurance cards. A health insurance card is a key document for care processing and can be digitally analyzed for patient onboarding, financial coverage information, cashless payments, and insurance claim processing. The health insurance card model analyzes health card images; extracts key information such as insurer, member, prescription, and group number; and returns a structured JSON representation. Health insurance cards can be presented in various formats and quality including phone-captured images, scanned documents, and digital PDFs.
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The Document Intelligence health insurance card model combines powerful Optical Character Recognition (OCR) capabilities with deep learning models to analyze and extract key information from US health insurance cards. A health insurance card is a key document for care processing and can be digitally analyzed for patient onboarding, financial coverage information, cashless payments, and insurance claim processing. The health insurance card model analyzes health card images; extracts key information such as insurer, member, prescription, and group number; and returns a structured JSON representation. Health insurance cards can be presented in various formats and quality including phone-captured images, scanned documents, and digital PDFs.
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***Sample health insurance card processed using Document Intelligence Studio***
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* An Azure subscription—you can [create one for free](https://azure.microsoft.com/free/cognitive-services/).
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* A [Document Intelligence instance](https://portal.azure.com/#create/Microsoft.CognitiveServicesFormRecognizer) in the Azure portal. You can use the free pricing tier (`F0`) to try the service. After your resource deploys, select **Go to resource** to get your key and endpoint.
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> [!div class="nextstepaction"]
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* Try processing your own forms and documents with the [Document Intelligence Studio](https://formrecognizer.appliedai.azure.com/studio).
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* Complete a [Document Intelligence quickstart](quickstarts/get-started-sdks-rest-api.md?view=doc-intel-3.1.0&preserve-view=true) and get started creating a document processing app in the development language of your choice.

articles/ai-services/document-intelligence/concept-invoice.md

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| KVKNumber(NL-only) | String | A unique identifier for businesses registered in the Netherlands|12345678|
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| TaxItems | Array | AN array that holds added tax information such as `CGST`, `IGST`, and `SGST`. This line item is currently only available for the Germany (`de`), Spain (`es`), Portugal (`pt`), and English Canada (`en-CA`) locales| |
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### Line items
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