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Copy file name to clipboardExpand all lines: articles/ai-services/document-intelligence/concept-analyze-document-response.md
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@@ -6,11 +6,10 @@ 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: 08/07/2024
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ms.date: 09/26/2024
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ms.author: vikurpad
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ms.custom:
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monikerRange: '>=doc-intel-3.0.0'
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---
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## Analyze document request
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The Document Intelligence APIs analyze images, PDFs, and other document files to extract and detect various content, layout, style, and semantic elements. The analyze operation is an async API. Submitting a document returns an **Operation-Location** header that contains the URL to poll for completion. When an analysis request completes successfully, the response contains the elements described in the [model data extraction](concept-model-overview.md#model-data-extraction).
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The Document Intelligence APIs analyze images, PDFs, and other document files to extract and detect various content, layout, style, and semantic elements. The `Analyze` operation is an async API. Submitting a document returns an **Operation-Location** header that contains the URL to poll for completion. When an analysis request completes successfully, the response contains the elements described in the [model data extraction](concept-model-overview.md#model-data-extraction).
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### Response elements
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## Analyze response
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The analyze response for each API returns different objects. API responses contain elements from component models where applicable.
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The `Analyze` response for each API returns different objects. API responses contain elements from component models where applicable.
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| Response content | Description | API |
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|--|--|--|
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|**styles**| Identified text element properties. | Read, Layout, General Document, Prebuilt, and Custom models|
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|**languages**| Identified language associated with each span of the text extracted | Read |
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|**tables**| Tabular content identified and extracted from the document. Tables relate to tables identified by the pretrained layout model. Content labeled as tables is extracted as structured fields in the documents object.| Layout, General Document, Invoice, and Custom models |
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|**figures**| Figures (charts, images) identified and extracted from the document, providing visual representations that aid in the understanding of complex information. | Layout model |
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|**sections**| Hierarchical document structure identified and extracted from the document. Section or subsection with the corresponding elements (paragraph, table, figure) attached to it. | Layout model |
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|**figures**| Figures (charts, images) identified and extracted from the document, providing visual representations that aid in the understanding of complex information. |The Layout model |
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|**sections**| Hierarchical document structure identified and extracted from the document. Section or subsection with the corresponding elements (paragraph, table, figure) attached to it. |The Layout model |
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|**keyValuePairs**| Key-value pairs recognized by a pretrained model. The key is a span of text from the document with the associated value. | General document and Invoice models |
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|**documents**| Fields recognized are returned in the `fields` dictionary within the list of documents| Prebuilt models, Custom models.|
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Figures (charts, images) in documents play a crucial role in complementing and enhancing the textual content, providing visual representations that aid in the understanding of complex information. The figures object detected by the Layout model has key properties like `boundingRegions` (the spatial locations of the figure on the document pages, including the page number and the polygon coordinates that outline the figure's boundary), `spans` (details the text spans related to the figure, specifying their offsets and lengths within the document's text. This connection helps in associating the figure with its relevant textual context), `elements` (the identifiers for text elements or paragraphs within the document that are related to or describe the figure) and `caption`, if any.
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When *output=figures* is specified during the initial `Analyze` operation, the service generates cropped images for all detected figures that can be accessed via `/analyeResults/{resultId}/figures/{figureId}`.
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`FigureId`will be included in each figure object, following an undocumented convention of `{pageNumber}.{figureIndex}` where `figureIndex` resets to one per page.
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`FigureId`is included in each figure object, following an undocumented convention of `{pageNumber}.{figureIndex}` where `figureIndex` resets to one per page.
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```json
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{
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| date | Date | ISO 8601 - YYYY-MM-DD | InvoiceDate: "5/7/2022" → "2022-05-07" |
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| time | Time | ISO 8601 - hh:mm:ss | TransactionTime: "9:45 PM" → "21:45:00" |
Copy file name to clipboardExpand all lines: articles/ai-services/document-intelligence/concept-custom-classifier.md
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title: Custom classification model - Document Intelligence
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title: Custom classification model - Document Intelligence
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titleSuffix: Azure AI services
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description: Use the custom classification model to train a model to identify and split the documents you process within your application.
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author: vkurpad
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ms.service: azure-ai-document-intelligence
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ms.topic: conceptual
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ms.date: 08/07/2024
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ms.date: 09/26/2024
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ms.author: lajanuar
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ms.custom:
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monikerRange: '>=doc-intel-3.1.0'
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---
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Custom classification models can analyze a single- or multi-file documents to identify if any of the trained document types are contained within an input file. Here are the currently supported scenarios:
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* A single file containing one document type, such as a loan application form.
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* A single file containing multiple document types. For instance, a loan application package that contains a loan application form, payslip, and bank statement.
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* A single file containing multiple instances of the same document. For instance, a collection of scanned invoices.
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✔️ The maximum allowed number of classes is `500`. The maximum allowed number of document samples per class is `100`.
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The model classifies each page of the input document, unless specified, to one of the classes in the labeled dataset. You can specify the page numbers to analyze in the input document as well. To set the threshold for your application, use the confidence score from the response.
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The model classifies each page of the input document, unless specified, to one of the classes in the labeled dataset. You can specify the page numbers to analyze in the input document as well. To set the threshold for your application, use the confidence score from the response.
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### Incremental training
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With custom models, you need to maintain access to the training dataset to update your classifier with new samples for an existing class, or add new classes. Classifier models now support incremental training where you can reference an existing classifier and append new samples for an existing class or add new classes with samples. Incremental training enables scenarios where data retention is a challenge and the classifier needs to be updated to align with changing business needs. Incremental training is supported with models trained with API version `2024-02-29-preview` and later.
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```
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As an example, the file list `car-maint.jsonl` contains the following files.
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```json
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{"file":"classifier/car-maint/Commercial Motor Vehicle - Adatum.pdf"}
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{"file":"classifier/car-maint/Commercial Motor Vehicle - Fincher.pdf"}
1. Next, you're going to fill out the **Create Document Intelligence** fields with the following values:
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1. Next, you're going to fill out the **`Create Document Intelligence`** fields with the following values:
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***Subscription**. Select your current subscription.
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***Resource group**. The [Azure resource group](/azure/cloud-adoption-framework/govern/resource-consistency/resource-access-management#what-is-an-azure-resource-group) that contains your resource. You can create a new group or add it to a pre-existing group.
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***Resource group**. The [Azure resource group](/azure/cloud-adoption-framework/govern/resource-consistency/resource-access-management#what-is-an-azure-resource-group) that contains your resource. You can create a new group or add it to an existing group.
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***Region**. Select your local region.
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***Name**. Enter a name for your resource. We recommend using a descriptive name, for example *YourNameFormRecognizer*.
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***Pricing tier**. The cost of your resource depends on the pricing tier you choose and your usage. For more information, see [pricing details](https://azure.microsoft.com/pricing/details/cognitive-services/). You can use the free pricing tier (F0) to try the service, and upgrade later to a paid tier for production.
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1. Open the **Program.cs** file.
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1. Delete the pre-existing code, including the line `Console.WriteLine("Hello World!")`. Copy and paste the code sample into your application's Program.cs file. Make sure you update the key variable with the value from your Azure portal Translator instance:
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1. Delete the existing code, including the line `Console.WriteLine("Hello World!")`. Copy and paste the code sample into your application's Program.cs file. Make sure you update the key variable with the value from your Azure portal Translator instance:
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```csharp
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usingSystem.Text;
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### Set up your Go environment
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You can use any text editor to write Go applications. We recommend using the latest version of [Visual Studio Code and the Go extension](/azure/developer/go/configure-visual-studio-code).
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You can use any text editor to write Go applications. We recommend using the latest version of [Visual Studio Code and Go extension](/azure/developer/go/configure-visual-studio-code).
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> [!TIP]
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>
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> If you're new to Go, try the [Get started with Go](/training/modules/go-get-started/) Learn module.
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1. Make sure the latest version of [Go](https://go.dev/doc/install) is installed:
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* Download the Go version for your operating system.
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* Download the `Go` programming language version for your operating system.
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* Once the download is complete, run the installer.
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* Open a command prompt and enter the following to confirm Go was installed:
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*[**Gradle**](https://docs.gradle.org/current/userguide/installation.html), version 6.8 or later.
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> [I ran into an issue](https://microsoft.qualtrics.com/jfe/form/SV_0Cl5zkG3CnDjq6O?PLanguage=Java&Product=Translator&Page=quickstart-translator&Section=set-up-your-java-environment) -->
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```
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> [I ran into an issue](https://microsoft.qualtrics.com/jfe/form/SV_0Cl5zkG3CnDjq6O?PLanguage=Python&Product=Translator&Page=quickstart-translator&Section=build-your-javascript-application) -->
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```
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> [My REST API call was successful](#next-steps) [I ran into an issue](https://microsoft.qualtrics.com/jfe/form/SV_0Cl5zkG3CnDjq6O?PLanguage=Java&Product=Translator&Page=quickstart-translator&Section=run-your-javascript-application) -->
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> [!NOTE]
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> We will also use a Python built-in package called json. It's used to work with JSON data.
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> [I ran into an issue](https://microsoft.qualtrics.com/jfe/form/SV_0Cl5zkG3CnDjq6O?PLanguage=Python&Product=Translator&Page=quickstart-translator&Section=build-your-python-application) -->
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