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@@ -41,14 +41,14 @@ The following image shows the landing page for Document Intelligence Studio.
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* Document Intelligence supports sophisticated analysis capabilities. The Studio allows one entry point (Analyze options button) for configuring the add-on capabilities with ease.
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* Depending on the document extraction scenario, configure the analysis range, document page range, optional detection, and premium detection features.
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:::image type="content" source="media/studio/analyze-options.png" alt-text="Screenshot of the analyzeoptions dialog window.":::
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:::image type="content" source="media/studio/analyze-options.png" alt-text="Screenshot of the analyze-options dialog window.":::
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> [!NOTE]
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> Font extraction is not visualized in Document Intelligence Studio. However, you can check the styles section of the JSON output for the font detection results.
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✔️ **Auto labeling documents with prebuilt models or one of your own models**
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* In custom extraction model labeling page, you can now auto label your documents using one of Document Intelligent Service prebuilt models or models you have trained before.
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* In custom extraction model labeling page, you can now auto label your documents using one of Document Intelligent Service prebuilt models or your trained models.
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:::image type="content" source="media/studio/auto-label.gif" alt-text="Animated screenshot showing auto labeling in Studio.":::
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@@ -64,7 +64,7 @@ The following image shows the landing page for Document Intelligence Studio.
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✔️ **Add test files directly to your training dataset**
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* Once you have trained a custom extraction model, make use of the test page to improve your model quality by uploading test documents to training dataset if needed.
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* Once you train a custom extraction model, make use of the test page to improve your model quality by uploading test documents to training dataset if needed.
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* If a low confidence score is returned for some labels, make sure they're correctly labeled. If not, add them to the training dataset and relabel to improve the model quality.
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***Layout**: Try out Document Intelligence's Layout feature to extract text, tables, selection marks, and structure information. Start with the [Studio Layout feature](https://formrecognizer.appliedai.azure.com/studio/layout). Explore with sample documents and your documents. Use the interactive visualization and JSON output to understand how the feature works. See the [Layout overview](concept-layout.md) to learn more and get started with the [Python SDK quickstart for Layout](quickstarts/get-started-sdks-rest-api.md?view=doc-intel-3.0.0&preserve-view=true#layout-model).
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***General Documents**: Try out Document Intelligence's General Documents feature to extract key-value pairs. Start with the [Studio General Documents feature](https://formrecognizer.appliedai.azure.com/studio/document). Explore with sample documents and your documents. Use the interactive visualization and JSON output to understand how the feature works. See the [General Documents overview](concept-general-document.md) to learn more and get started with the [Python SDK quickstart for Layout](quickstarts/get-started-sdks-rest-api.md?view=doc-intel-3.0.0&preserve-view=true#general-document-model).
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***Prebuilt models**: Document Intelligence's prebuilt models enable you to add intelligent document processing to your apps and flows without having to train and build your own models. As an example, start with the [Studio Invoice feature](https://formrecognizer.appliedai.azure.com/studio/prebuilt?formType=invoice). Explore with sample documents and your documents. Use the interactive visualization, extracted fields list, and JSON output to understand how the feature works. See the [Models overview](concept-model-overview.md) to learn more and get started with the [Python SDK quickstart for Prebuilt Invoice](quickstarts/get-started-sdks-rest-api.md?view=doc-intel-3.0.0&preserve-view=true#prebuilt-model).
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***Custom extraction models**: Document Intelligence's custom models enable you to extract fields and values from models trained with your data, tailored to your forms and documents. Create standalone custom models or combine two or more custom models to create a composed model to extract data from multiple form types. Start with the [Studio Custom models feature](https://formrecognizer.appliedai.azure.com/studio/custommodel/projects). Use the help wizard, labeling interface, training step, and visualizations to understand how the feature works. Test the custom model with your sample documents and iterate to improve the model. See the [Custom models overview](concept-custom.md) to learn more.
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