Merge pull request #4881 from laujan/dybe-4713-4791-4502-4847-pr-updates

Dybe 4713 4791 4502 4847 pr updates

Author: Stacyrch140

articles/ai-services/content-understanding/glossary.md

@@ -24,6 +24,8 @@ ms.author: lajanuar
 | **Field schema** | A formal description of the fields to extract from the input. It specifies the name, description, value type, generation method, and more for each field. |
 | **Generation method** | The process of determining the extracted value of a specified field. Content Understanding supports: <br/> &bullet; **Extract**: Directly extract values from the input content, such as dates from receipts or item details from invoices. <br/> &bullet; **Classify**: Classify content into predefined categories, such as call sentiment or chart type. <br/> &bullet; **Generate**: Generate values from input data, such as summarizing an audio conversation or generating scene descriptions from videos. |
 | **Span** | A reference indicating the location of an element (for example, field, word) within the extracted Markdown content. A character offset and length represent a span. Different programming languages use various character encodings, which can affect the exact offset and length values for Unicode text. To avoid confusion, spans are only returned if the desired encoding is explicitly specified in the request. Some elements can map to multiple spans if they aren't contiguous in the markdown (for example, page). |
+| **Processing Location** | An API request parameter that defines the geographic region where Azure AI Services analyzes your data. You can choose from three options: `geography`, `dataZone`, and `global` to control where processing occurs. This setting helps meet data residency requirements and optimize performance or scalability based on your needs. For more information, *see* the Content Understanding API reference documentation.
 | **Grounding source** | The specific regions in content where a value was generated. It has different representations depending on the file type: <br>&bullet; **Image** - A polygon in the image, often an axis-aligned rectangle (bounding box). <br>&bullet; **PDF/TIFF** - A polygon on a specific page, often a quadrilateral. <br>&bullet; **Audio** - A start and end time range. <br>&bullet; **Video** - A start and end time range with an optional polygon in each frame, often a bounding box.|
+| **Person directory** | A structured way to store face data for recognition tasks. You can add individual faces to the directory and later search for visually similar faces. You can also create person profiles, associate faces to them, and match new face images to known individuals. This setup supports both flexible face matching and identity recognition across images and videos. |
 | **Confidence score** | The level of certainty that the extracted data is accurate. |
 | **Category** | A distinct class within a classifier used to group similar input files based on shared characteristics or features. |

articles/ai-services/content-understanding/image/overview.md

@@ -28,7 +28,7 @@ Azure AI Content Understanding standardizes the extraction of data from images,
 
 * **Shelf analysis and inventory management:** Detect, count, and extract specific details about retail products, optimizing operations, and improving customer satisfaction by ensuring products are well-stocked and properly organized.
 
-## Key Benefits
+## Key benefits
 
 Content Understanding offers several key benefits for extracting information from images, including,
 
@@ -38,31 +38,24 @@ Content Understanding offers several key benefits for extracting information fro
 
 * **Faster and more cost-effective automation:**  The extracting of only the necessary fields enables Content Understanding to streamlines automation. Thus allowing organizations to scale their data processing workflows efficiently and reduce the storage and processing of irrelevant data.
 
+:::image type="content" source="../media/image/image-flow-diagram.jpg" alt-text="Screenshot of a data flow diagram for image processing in content understanding.":::
 
-## Input requirements
-For detailed information on supported input file formats, refer to our [Service quotas and limits](../service-limits.md) page.
+## Get started
+
+Get started with processing images with Content Understanding by following our [REST API quickstart](LINK TO IMAGE TAB) or visiting [Azure AI Foundry](https://aka.ms/cu-landing) for a no code experience. 
 
 > [!NOTE]
 > For best results, image schema should only be used to process non-document-based images.
 > Text heavy images of documents should be processed using a document schema.
 > Use cases that require extraction of text from document images or scanned documents should be processed using a document field extraction schema.
 
-## Supported languages and regions
-For a detailed list of supported languages and regions, visit our [Language and region support](../language-region-support.md) page.
-
-## Supported field types
-For detailed information on supported field types, refer to our [Service quotas and limits](../service-limits.md#field-type-limits) page.
-
-## Data privacy and security
-
-As with all the Azure AI services, developers using the Content Understanding service should be aware of Microsoft's policies on customer data. See our [**Data, protection and privacy**](https://www.microsoft.com/trust-center/privacy) page to learn more.
 
 > [!IMPORTANT]
 > If you're using Microsoft products or services to process Biometric Data, you're responsible for: (i) providing notice to data subjects, including with respect to retention periods and destruction; (ii) obtaining consent from data subjects; and (iii) deleting the Biometric Data, all as appropriate, and required under applicable Data Protection Requirements. "Biometric Data" has the meaning articulated in Article 4 of the GDPR and, if applicable, equivalent terms in other data protection requirements. For related information, see [Data and Privacy for Face](/legal/cognitive-services/face/data-privacy-security).
 
 ## Next steps
 
-* Try processing your video content using Content Understanding in [Azure AI Foundry portal](https://aka.ms/cu-landing).
-* Learn to analyze video content [**analyzer templates**](../quickstart/use-ai-foundry.md).
-* Review code samples: [**image, text, and table, content extraction**](https://github.com/Azure-Samples/azure-ai-search-with-content-understanding-python#samples).
-* Review code sample: [**analyzer templates**](https://github.com/Azure-Samples/azure-ai-content-understanding-python/tree/main/analyzer_templates).
+* For guidance on optimizing your Content Understanding implementations, including schema design tips, see our detailed [Best practices guide](../concepts/best-practices.md).
+* For detailed information on supported input image formats, *see* [Service quotas and limits](../service-limits.md).
+* To review code samples, *see* [**analyzer templates**](https://github.com/Azure-Samples/azure-ai-content-understanding-python/tree/main/analyzer_templates).
+* For more information on trust and security, *see* [Data, protection, and privacy policy](https://www.microsoft.com/trust-center/privacy).

articles/ai-services/content-understanding/media/image/image-flow-diagram.jpg

@@ -70,12 +70,11 @@ See [Quickstart](quickstart/use-ai-foundry.md) for more examples.
 |Grounding source| Content Understanding identifies the specific regions in the content where the value was generated from. Source grounding allows users in automation scenarios to quickly verify the correctness of the field values, leading to higher confidence in the extracted data. |
 |Confidence score | Content Understanding provides confidence scores from 0 to 1 to estimate the reliability of the results. High scores indicate accurate data extraction, enabling straight-through processing in automation workflows.|
 
-
 ## Responsible AI
 
  Azure AI Content Understanding is designed to guard against processing harmful content, such as graphic violence and gore, hateful speech and bullying, exploitation, abuse, and more. For more information and a full list of prohibited content, *see* our [**Transparency note**](/legal/cognitive-services/content-understanding/transparency-note?toc=/azure/ai-services/content-understanding/toc.json&bc=/azure/ai-services/content-understanding/breadcrumb/toc.json) and our [**Code of Conduct**](https://aka.ms/AI-CoC).
 
-### Modified Content Filtering
+### Modified content filtering
 
 Content Understanding now supports modified content filtering for approved customers. The subscription IDs with approved modified content filtering impacts Content Understanding output. By default, Content Understanding employs a content filtering system that identifies specific risk categories for potentially harmful content in both submitted prompts and generated outputs. Modified content filtering allows the system to annotate rather than block potentially harmful output, giving you the ability to determine how to handle potentially harmful content. For more information on content filter types, *see* [Content filtering: filter types](../openai/concepts/content-filter.md#content-filter-types).
 
@@ -84,7 +83,7 @@ Content Understanding now supports modified content filtering for approved custo
 > * Apply for modified content filters via this form: [Azure OpenAI Limited Access Review: Modified Content Filters](https://ncv.microsoft.com/uEfCgnITdR).
 > * For more information, *see* [**Content Filtering**](../openai/concepts/content-filter.md).
 
-To learn more about how to add modified content filtering to your requests, *see* our [REST API quickstart](quickstart/use-rest-api.md#modified-content-filtering).
+To learn more about how to add modified content filtering to your requests, *see* our [REST API quickstart](quickstart/use-rest-api.md).
 
 ## Data privacy and security
 Developers using the Content Understanding service should review Microsoft's policies on customer data. For more information, visit our [**Data, protection and privacy**](https://www.microsoft.com/trust-center/privacy) page.
@@ -93,6 +92,7 @@ Developers using the Content Understanding service should review Microsoft's pol
 > If you're using Microsoft products or services to process Biometric Data, you're responsible for: (i) providing notice to data subjects, including with respect to retention periods and destruction; (ii) obtaining consent from data subjects; and (iii) deleting the Biometric Data, all as appropriate, and required under applicable Data Protection Requirements. "Biometric Data" has the meaning articulated in Article 4 of the GDPR and, if applicable, equivalent terms in other data protection requirements. For related information, see [Data and Privacy for Face](/legal/cognitive-services/face/data-privacy-security).
 
 ## Getting started
+
 Our quickstart guides help you quickly start using the Content Understanding service:
 
 * [**Azure AI Foundry portal Quickstart**](quickstart/use-ai-foundry.md)