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Copy file name to clipboardExpand all lines: articles/applied-ai-services/form-recognizer/concept-composed-models.md
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@@ -42,7 +42,7 @@ With composed models, you can assign multiple custom models to a composed model
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::: moniker range="form-recog-3.0.0"
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With the introduction of [****custom classifier models****](./concept-custom-classifier.md), you can choose to use [**composed models**](./concept-composed-models.md) or the classifier model as an explicit step before analysis. For a deeper understanding of when to use a classifier or composed model, _see_[**Custom classifier models**](concept-custom-classifier.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).
Custom classifier models are deep-learning-model types that combine layout and language features to accurately detect and identify documents you process within your application. Custom classifier models can classify each page in an input file to identify the document(s) within and can also identify multiple documents or multiple instances of a single document within an input file.
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Custom classification models are deep-learning-model types that combine layout and language features to accurately detect and identify documents you process within your application. Custom classification models can classify each page in an input file to identify the document(s) within and can also identify multiple documents or multiple instances of a single document within an input file.
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## Model capabilities
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Custom classifier 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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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. For instance, a loan application form.
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* A single file containing multiple documents. For instance, a loan application package containing 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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Training a custom classifier model requires at least two distinct classes and a minimum of five samples per class.
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Training a custom classifier requires at least two distinct classes and a minimum of five samples per class.
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### Compare custom classifier and composed models
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### Compare custom classification and composed models
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A custom classifier model can replace [a composed model](concept-composed-models.md) in some scenarios but there are a few differences to be aware of:
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A custom classification model can replace [a composed model](concept-composed-models.md) in some scenarios but there are a few differences to be aware of:
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| Capability | Custom classifier process | Composed model process |
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|--|--|--|
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|Analyze a single document of unknown type belonging to one of the types trained for extraction model processing.|● Requires multiple calls. </br> ● Call the classifier models based on the document class. This step allows for a confidence-based check before invoking the extraction model analysis.</br> ● Invoke the extraction model. |● Requires a single call to a composed model containing the model corresponding to the input document type. |
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|Analyze a single document of unknown type belonging to one of the types trained for extraction model processing.|● Requires multiple calls. </br> ● Call the classification model based on the document class. This step allows for a confidence-based check before invoking the extraction model analysis.</br> ● Invoke the extraction model. |● Requires a single call to a composed model containing the model corresponding to the input document type. |
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|Analyze a single document of unknown type belonging to several types trained for extraction model processing.|●Requires multiple calls.</br> ● Make a call to the classifier that ignores documents not matching a designated type for extraction.</br> ● Invoke the extraction model. |● Requires a single call to a composed model. The service selects a custom model within the composed model with the highest match.</br> ● A composed model can't ignore documents.|
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|Analyze a file containing multiple documents of known or unknown type belonging to one of the types trained for extraction model processing.|● Requires multiple calls. </br> ● Call the extraction model for each identified document in the input file.</br> ● Invoke the extraction model. |● Requires a single call to a composed model.</br> ● The composed model invokes the component model once on the first instance of the document. </br> ●The remaining documents are ignored. |
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## Language support
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Classifier models currently only support English language documents.
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Classification models currently only support English language documents.
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## Best practices
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Custom classifier models require a minimum of five samples per class to train. If the classes are similar, adding extra training samples improves model accuracy.
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Custom classification models require a minimum of five samples per class to train. If the classes are similar, adding extra training samples improves model accuracy.
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## Training a model
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Custom classifier models are only available in the [v3.0 API](v3-migration-guide.md) starting with API version ```2023-02-28-preview```. [Form Recognizer 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 only available in the [v3.0 API](v3-migration-guide.md) starting with API version ```2023-02-28-preview```. [Form Recognizer 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 classifier model.
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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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Form Recognizer uses advanced machine learning technology to identify documents, detect and extract information from forms and documents, and return the extracted data in a structured JSON output. With Form Recognizer, you can use document analysis models, pre-built/pre-trained, or your trained standalone custom models.
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Custom models now include [custom classifier models](./concept-custom-classifier.md) for scenarios where you need to identify the document type prior to invoking the extraction model. Classifier models are available starting with the ```2023-02-28-preview``` API. A classifier model can be paired with a custom extraction model to analyze and extract fields from forms and documents specific to your business to create a document processing solution. Standalone custom extraction models can be combined to create [composed models](concept-composed-models.md).
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Custom models now include [custom classification models](./concept-custom-classifier.md) for scenarios where you need to identify the document type prior to invoking the extraction model. Classifier models are available starting with the ```2023-02-28-preview``` API. A classification model can be paired with a custom extraction model to analyze and extract fields from forms and documents specific to your business to create a document processing solution. Standalone custom extraction models can be combined to create [composed models](concept-composed-models.md).
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::: moniker range="form-recog-3.0.0"
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|Document variations | Requires a model per each variation | Uses a single model for all variations |
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|Language support | Multiple [language support](language-support.md#read-layout-and-custom-form-template-model)| English, with preview support for Spanish, French, German, Italian and Dutch [language support](language-support.md#custom-neural-model)|
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### Custom classifier model
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### Custom classification model
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Document classification is a new scenario supported by Form Recognizer with the ```2023-02-28-preview``` API. Document classifier supports classification and splitting scenarios. Train a classifier model to identify the different types of documents your application supports. The input file for the classifier model can contain multiple documents and classifies each document within an associated page range. See [custom classification](concept-custom-classifier.md) models to learn more.
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Document classification is a new scenario supported by Form Recognizer with the ```2023-02-28-preview``` API. the document classifier API supports classification and splitting scenarios. Train a classification model to identify the different types of documents your application supports. The input file for the classification model can contain multiple documents and classifies each document within an associated page range. See [custom classification](concept-custom-classifier.md) models to learn more.
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## Custom model tools
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1. Review and create your project.
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1. Label your documents to build and test your custom classifier model.
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1. Label your documents to build and test your custom classification model.
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> [!div class="nextstepaction"]
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> [Try Form Recognizer Studio](https://formrecognizer.appliedai.azure.com/studio/document-classifier/projects)
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|**Custom models**||
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|[Custom model (overview)](#custom-models)| Extract data from forms and documents specific to your business. Custom models are trained for your distinct data and use cases. |
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|[Custom extraction models](#custom-extraction)|●**Custom template models** use layout cues to extract values from documents and are suitable to extract fields from highly structured documents with defined visual templates.</br>●**Custom neural models** are trained on various document types to extract fields from structured, semi-structured and unstructured documents.|
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| [Custom classifier model](#custom-classifier)| The **Custom classifier model** can classify each page in an input file to identify the document(s) within and can also identify multiple documents or multiple instances of a single document within an input file.
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| [Custom classification model](#custom-classifier)| The **Custom classification model** can classify each page in an input file to identify the document(s) within and can also identify multiple documents or multiple instances of a single document within an input file.
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| [Composed models](#composed-models) | Combine several custom models into a single model to automate processing of diverse document types with a single composed model.
The Layout analysis model analyzes and extracts text, tables, selection marks, and other structure elements like titles, section headings, page headers, page footers, and more.
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The general document model is ideal for extracting common key-value pairs from forms and documents. It's a pre-trained model and can be directly invoked via the REST API and the SDKs. You can use the general document model as an alternative to training a custom model.
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The general document model is ideal for extracting common key-value pairs from forms and documents. It's a pretrained model and can be directly invoked via the REST API and the SDKs. You can use the general document model as an alternative to training a custom model.
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***Sample document processed using the [Form Recognizer Studio](https://formrecognizer.appliedai.azure.com/studio/document)***:
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### Health insurance card
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:::image type="content" source="media/studio/health-insurance-logo.png" alt-text="Screenshot of insurance card analysis in Form Recognizer Studio.":::
The 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.
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The W-2 form model extracts key information reported in each box on a W-2 form. The model supports standard and customized forms from 2018 to the present, including single and multiple forms on one page.
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The invoice model automates processing of invoices to extracts customer name, billing address, due date, and amount due, line items and other key data. Currently, the model supports English, Spanish, German, French, Italian, Portuguese, and Dutch invoices.
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Use the receipt model to scan sales receipts for merchant name, dates, line items, quantities, and totals from printed and handwritten receipts. The version v3.0 also supports single-page hotel receipt processing.
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Use the Identity document (ID) model to process U.S. Driver's Licenses (all 50 states and District of Columbia) and biographical pages from international passports (excluding visa and other travel documents) to extract key fields.
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Custom document models analyze and extract data from forms and documents specific to your business. They're trained to recognize form fields within your distinct content and extract key-value pairs and table data. You only need five examples of the same form type to get started.
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Custom extraction model can be one of two types, **custom template** or **custom neural**. To create a custom extraction model, label a dataset of documents with the values you want extracted and train the model on the labeled dataset. You only need five examples of the same form or document type to get started.
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The custom classifier model enables you to identify the document type prior to invoking the extraction model. The classifier model is available starting with the 2023-02-28-preview. Training a custom classifier model requires at least two distinct classes and a minimum of five samples per class.
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The custom classification model enables you to identify the document type prior to invoking the extraction model. The classification model is available starting with the 2023-02-28-preview. Training a custom classification model requires at least two distinct classes and a minimum of five samples per class.
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