Merge pull request #177849 from PatrickFarley/freshness-pass

Court72 · web-flow · commit 2705f3835be2 · 2021-11-02T08:20:24.000-06:00
[cog svcs] Freshness pass
diff --git a/articles/cognitive-services/Computer-vision/Tutorials/CSharpTutorial.md b/articles/cognitive-services/Computer-vision/Tutorials/CSharpTutorial.md
@@ -8,14 +8,14 @@ manager: nolachar
 ms.service: cognitive-services
 ms.subservice: computer-vision
 ms.topic: sample
-ms.date: 04/17/2019
+ms.date: 10/27/2021
 ms.author: pafarley
 ms.custom: "seodec18, devx-track-csharp"
 ---
 
 # Sample: Explore an image processing app with C#
 
-Explore a basic Windows application that uses Computer Vision to perform optical character recognition (OCR), create smart-cropped thumbnails, plus detect, categorize, tag and describe visual features, including faces, in an image. The below example lets you submit an image URL or a locally stored file. You can use this open source example as a template for building your own app for Windows using the Computer Vision API and Windows Presentation Foundation (WPF), a part of .NET Framework.
+Explore a basic Windows application that uses Computer Vision to perform optical character recognition (OCR), create smart-cropped thumbnails, and detect, categorize, tag and describe visual features, including faces, in an image. The below example lets you submit an image URL or a locally stored file. You can use this open source example as a template for building your own app for Windows using the Computer Vision API and Windows Presentation Foundation (WPF), a part of the .NET Framework.
 
 > [!div class="checklist"]
 > * Get the sample app from GitHub
@@ -44,7 +44,7 @@ git clone --recurse-submodules https://github.com/Microsoft/Cognitive-Vision-Win
 ```
 
 > [!IMPORTANT]
-> Do not download this repository as a ZIP. Git doesn't include submodules when downloading a repository as a ZIP.
+> Do not download this repository as a _.zip_ file. Git doesn't include submodules when downloading a repository as a _.zip_.
 
 ### Get optional sample images
 
diff --git a/articles/cognitive-services/Computer-vision/concept-object-detection.md b/articles/cognitive-services/Computer-vision/concept-object-detection.md
@@ -9,14 +9,14 @@ manager: nitinme
 ms.service: cognitive-services
 ms.subservice: computer-vision
 ms.topic: conceptual
-ms.date: 04/17/2019
+ms.date: 10/27/2021
 ms.author: pafarley
 ms.custom: seodec18
 ---
 
 # Detect common objects in images
 
-Object detection is similar to [tagging](concept-tagging-images.md), but the API returns the bounding box coordinates (in pixels) for each object found. For example, if an image contains a dog, cat and person, the Detect operation will list those objects together with their coordinates in the image. You can use this functionality to process the relationships between the objects in an image. It also lets you determine whether there are multiple instances of the same tag in an image.
+Object detection is similar to [tagging](concept-tagging-images.md), but the API returns the bounding box coordinates (in pixels) for each object found in the image. For example, if an image contains a dog, cat and person, the Detect operation will list those objects with their coordinates in the image. You can use this functionality to process the relationships between the objects in an image. It also lets you determine whether there are multiple instances of the same object in an image.
 
 The Detect API applies tags based on the objects or living things identified in the image. There is currently no formal relationship between the tagging taxonomy and the object detection taxonomy. At a conceptual level, the Detect API only finds objects and living things, while the Tag API can also include contextual terms like "indoor", which can't be localized with bounding boxes.
 
diff --git a/articles/cognitive-services/Computer-vision/language-support.md b/articles/cognitive-services/Computer-vision/language-support.md
@@ -8,28 +8,29 @@ manager: nitinme
 ms.service: cognitive-services
 ms.subservice: computer-vision
 ms.topic: conceptual
-ms.date: 04/17/2019
+ms.date: 10/27/2021
 ms.author: pafarley
 ---
 
 # Language support for Computer Vision
 
-Some features of Computer Vision support multiple languages; any features not mentioned here only support English.
+Some capabilities of Computer Vision support multiple languages; any capabilities not mentioned here only support English.
 
 ## Optical Character Recognition (OCR)
 
-The Computer Vision OCR APIs support several languages. Read can extract text from images and documents with mixed languages, including same text line without requiring a language parameter. See the [Optical Character Recognition (OCR) overview](overview-ocr.md) for more information.
+The Computer Vision OCR APIs support many languages. Read can extract text from images and documents with mixed languages, including from the same text line, without requiring a language parameter. See the [Optical Character Recognition (OCR) overview](overview-ocr.md) for more information.
 
 
 > [!NOTE]
 > **Language code optional**
 >
-> Read OCR's deep learning based universal models extract all multi-lingual text in your documents, including text lines with mixed languages, and do not require specifying a language code. Do not provide the language code as the parameter unless you are sure about the language and want to force the service to apply only the relevant model. Otherwise, the service may return incomplete and incorrect text.
+> Read OCR's deep-learning-based universal models extract all multi-lingual text in your documents, including text lines with mixed languages, and do not require specifying a language code. Do not provide the language code as the parameter unless you are sure about the language and want to force the service to apply only the relevant model. Otherwise, the service may return incomplete and incorrect text.
 
-See [How to specify the model version](./Vision-API-How-to-Topics/call-read-api.md#determine-how-to-process-the-data-optional) to use the preview languages and features. The preview model includes any enhancements to the currently GA version.
+The preview model includes any enhancements to the current GA version of the API. See [How to specify the model version](./Vision-API-How-to-Topics/call-read-api.md#determine-how-to-process-the-data-optional) to use the preview languages and features. 
 
 ### Handwritten languages
-The following table lists the handwritten languages supported by Read.
+
+The following table lists the languages supported by Read for handwritten text.
 
 |Language| Language code (optional) | Read |
 |:-----|:----:|:-----|
@@ -42,7 +43,8 @@ The following table lists the handwritten languages supported by Read.
 |Spanish |`es`|✅ preview |
 
 ### Print languages
-The following table lists the print languages supported by the OCR APIs.
+
+The following table lists the languages supported by the OCR APIs for printed text.
 
 |Language| Language code (optional) | Read | OCR |
 |:-----|:----:|:-----|:---:|
@@ -174,7 +176,7 @@ The following table lists the print languages supported by the OCR APIs.
 
 ## Image analysis
 
-Some actions of the [Analyze - Image](https://westcentralus.dev.cognitive.microsoft.com/docs/services/computer-vision-v3-1-ga/operations/56f91f2e778daf14a499f21b) API can return results in other languages, specified with the `language` query parameter. Other actions return results in English regardless of what language is specified, and others throw an exception for unsupported languages. Actions are specified with the `visualFeatures` and `details` query parameters; see the [Overview](overview-image-analysis.md) for a list of all the actions you can do with image analysis. Languages for tagging are only available in API version 3.2 or later.
+Some features of the [Analyze - Image](https://westcentralus.dev.cognitive.microsoft.com/docs/services/computer-vision-v3-1-ga/operations/56f91f2e778daf14a499f21b) API can return results in other languages, specified with the `language` query parameter. Other actions return results in English regardless of what language is specified, and others throw an exception for unsupported languages. Actions are specified with the `visualFeatures` and `details` query parameters; see the [Overview](overview-image-analysis.md) for a list of all the actions you can do with image analysis. Languages for tagging are only available in API version 3.2 or later.
 
 |Language | Language code | Categories | Tags | Description | Adult | Brands | Color | Faces | ImageType | Objects | Celebrities | Landmarks |
 |:---|:---:|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
diff --git a/articles/cognitive-services/Content-Moderator/Review-Tool-User-Guide/human-in-the-loop.md b/articles/cognitive-services/Content-Moderator/Review-Tool-User-Guide/human-in-the-loop.md
@@ -6,7 +6,7 @@ services: cognitive-services
 author: PatrickFarley
 manager: mikemcca
 
-ms.date: 03/15/2019
+ms.date: 10/27/2021
 ms.service: cognitive-services
 ms.subservice: content-moderator
 ms.topic: conceptual
diff --git a/articles/cognitive-services/Content-Moderator/image-moderation-api.md b/articles/cognitive-services/Content-Moderator/image-moderation-api.md
@@ -9,7 +9,7 @@ manager: nitinme
 ms.service: cognitive-services
 ms.subservice: content-moderator
 ms.topic: conceptual
-ms.date: 04/14/2020
+ms.date: 10/27/2021
 ms.author: pafarley
 
 ---
diff --git a/articles/cognitive-services/Content-Moderator/index.yml b/articles/cognitive-services/Content-Moderator/index.yml
@@ -12,7 +12,7 @@ metadata:
   ms.topic: landing-page # Required
   author: PatrickFarley #Required; your GitHub user alias, with correct capitalization.
   ms.author: pafarley #Required; microsoft alias of author; optional team alias.
-  ms.date: 10/24/2019 #Required; mm/dd/yyyy format.
+  ms.date: 10/27/2021 #Required; mm/dd/yyyy format.
 
 # linkListType: architecture | concept | deploy | download | get-started | how-to-guide | learn | overview | quickstart | reference | tutorial | video | whats-new
 
diff --git a/articles/cognitive-services/Content-Moderator/language-support.md b/articles/cognitive-services/Content-Moderator/language-support.md
@@ -8,7 +8,7 @@ manager: nitinme
 ms.service: cognitive-services
 ms.subservice: content-moderator
 ms.topic: conceptual
-ms.date: 01/10/2019
+ms.date: 10/27/2021
 ms.author: pafarley
 
 ---
diff --git a/articles/cognitive-services/Content-Moderator/samples-dotnet.md b/articles/cognitive-services/Content-Moderator/samples-dotnet.md
@@ -9,7 +9,7 @@ manager: nitinme
 ms.service: cognitive-services
 ms.subservice: content-moderator
 ms.topic: sample
-ms.date: 01/10/2019
+ms.date: 10/27/2021
 ms.author: pafarley
 
 ---
diff --git a/articles/cognitive-services/Content-Moderator/text-moderation-api.md b/articles/cognitive-services/Content-Moderator/text-moderation-api.md
@@ -9,7 +9,7 @@ manager: nitinme
 ms.service: cognitive-services
 ms.subservice: content-moderator
 ms.topic: conceptual
-ms.date: 05/18/2020
+ms.date: 10/27/2021
 ms.author: pafarley
 
 ---
diff --git a/articles/cognitive-services/Content-Moderator/try-text-api.md b/articles/cognitive-services/Content-Moderator/try-text-api.md
@@ -10,7 +10,7 @@ manager: nitinme
 ms.service: cognitive-services
 ms.subservice: content-moderator
 ms.topic: conceptual
-ms.date: 05/29/2019
+ms.date: 10/27/2021
 ---
 
 # Moderate text from the API console
diff --git a/articles/cognitive-services/Content-Moderator/video-moderation-api.md b/articles/cognitive-services/Content-Moderator/video-moderation-api.md
@@ -9,7 +9,7 @@ manager: nitinme
 ms.service: cognitive-services
 ms.subservice: content-moderator
 ms.topic: how-to
-ms.date: 05/18/2020
+ms.date: 10/27/2021
 ms.author: pafarley
 ms.custom: devx-track-csharp
 
diff --git a/articles/cognitive-services/Custom-Vision-Service/export-your-model.md b/articles/cognitive-services/Custom-Vision-Service/export-your-model.md
@@ -9,7 +9,7 @@ manager: nitinme
 ms.service: cognitive-services
 ms.subservice: custom-vision
 ms.topic: conceptual
-ms.date: 03/21/2019
+ms.date: 10/27/2021
 ms.author: pafarley
 ---
 
diff --git a/articles/cognitive-services/Custom-Vision-Service/index.yml b/articles/cognitive-services/Custom-Vision-Service/index.yml
@@ -12,7 +12,7 @@ metadata:
   ms.topic: landing-page # Required
   author: PatrickFarley #Required; your GitHub user alias, with correct capitalization.
   ms.author: pafarley #Required; microsoft alias of author; optional team alias.
-  ms.date: 09/05/2019 #Required; mm/dd/yyyy format.
+  ms.date: 10/27/2021 #Required; mm/dd/yyyy format.
 
 # linkListType: architecture | concept | deploy | download | get-started | how-to-guide | learn | overview | quickstart | reference | tutorial | video | whats-new
 
diff --git a/articles/cognitive-services/Custom-Vision-Service/select-domain.md b/articles/cognitive-services/Custom-Vision-Service/select-domain.md
@@ -46,6 +46,8 @@ From the settings tab of your Custom Vision project, you can select a domain for
 
 The models generated by compact domains can be exported to run locally. In the Custom Vision 3.4 public preview API, you can get a list of the exportable platforms for compact domains by calling the GetDomains API.
 
+All of the following domains support export in ONNX, TensorFlow,TensorFlowLite, TensorFlow.js, CoreML, and VAIDK formats, with the exception that the **Object Detection General (compact)** domain does not support VAIDK.
+
 Model performance varies by selected domain. In the table below, we report the model size and inference time on Intel Desktop CPU and NVidia GPU \[1\]. These numbers don't include preprocessing and postprocessing time.
 
 |Task|Domain|ID|Model Size|CPU inference time|GPU inference time|
diff --git a/articles/cognitive-services/Custom-Vision-Service/suggested-tags.md b/articles/cognitive-services/Custom-Vision-Service/suggested-tags.md
@@ -9,13 +9,13 @@ manager: nitinme
 ms.service: cognitive-services
 ms.subservice: custom-vision
 ms.topic: conceptual
-ms.date: 10/29/2019
+ms.date: 10/27/2021
 ms.author: pafarley
 ---
 
 # Label images faster with Smart Labeler
 
-In this guide, you'll learn how to use Smart Labeler to generate suggested tags for images. This lets you label a large number of images more quickly when training a Custom Vision model.
+In this guide, you'll learn how to use Smart Labeler to generate suggested tags for images. This lets you label a large number of images more quickly when you're training a Custom Vision model.
 
 When you tag images for a Custom Vision model, the service uses the latest trained iteration of the model to predict the labels of untagged images. It then shows these predictions as suggested tags, based on the selected confidence threshold and prediction uncertainty. You can then either confirm or change the suggestions, speeding up the process of manually tagging the images for training.
 
diff --git a/articles/cognitive-services/Custom-Vision-Service/test-your-model.md b/articles/cognitive-services/Custom-Vision-Service/test-your-model.md
@@ -1,21 +1,21 @@
 ---
 title: Test and retrain a model - Custom Vision Service
 titleSuffix: Azure Cognitive Services
-description: This article will show you how to test an image and then use it to retrain your model in the Custom Vision service.
+description: Learn how to test an image and then use it to re-train your model in the Custom Vision service.
 services: cognitive-services
 author: PatrickFarley
 manager: nitinme
 
 ms.service: cognitive-services
 ms.subservice: custom-vision
 ms.topic: conceptual
-ms.date: 03/21/2019
+ms.date: 10/27/2021
 ms.author: pafarley
 ---
 
 # Test and retrain a model with Custom Vision Service
 
-After you train your model, you can quickly test it using a locally stored image or an online image. The test uses the most recently trained iteration.
+After you train your model, you can quickly test it using a locally stored image or an online image. The test uses the most recently trained iteration of your model.
 
 ## Test your model
 
diff --git a/articles/cognitive-services/Custom-Vision-Service/use-prediction-api.md b/articles/cognitive-services/Custom-Vision-Service/use-prediction-api.md
@@ -9,14 +9,14 @@ manager: nitinme
 ms.service: cognitive-services
 ms.subservice: custom-vision
 ms.topic: conceptual
-ms.date: 04/02/2019
+ms.date: 10/27/2021
 ms.author: pafarley
 ms.custom: devx-track-csharp
 ---
 
 # Use your model with the prediction API
 
-After you've train your model, you can test images programmatically by submitting them to the Prediction API endpoint.
+After you've train your model, you can test it programmatically by submitting images to the Prediction API endpoint.
 
 > [!NOTE]
 > This document demonstrates using C# to submit an image to the Prediction API. For more information and examples, see the [Prediction API reference](https://southcentralus.dev.cognitive.microsoft.com/docs/services/Custom_Vision_Prediction_3.0/operations/5c82db60bf6a2b11a8247c15).
diff --git a/articles/cognitive-services/Face/concepts/face-detection.md b/articles/cognitive-services/Face/concepts/face-detection.md
@@ -1,15 +1,15 @@
 ---
 title: "Face detection and attributes concepts"
 titleSuffix: Azure Cognitive Services
-description: Face detection is the action of locating human faces in an image and optionally returning different kinds of face-related data.
+description: Learn more about face detection; face detection is the action of locating human faces in an image and optionally returning different kinds of face-related data.
 services: cognitive-services
 author: PatrickFarley
 manager: nitime
 
 ms.service: cognitive-services
 ms.subservice: face-api
 ms.topic: conceptual
-ms.date: 04/26/2019
+ms.date: 10/27/2021
 ms.author: pafarley
 ---
 
@@ -62,18 +62,27 @@ Attributes are a set of features that can optionally be detected by the [Face -
 
 Use the following tips to make sure that your input images give the most accurate detection results:
 
-* The supported input image formats are JPEG, PNG, GIF for the first frame, and BMP.
+* The supported input image formats are JPEG, PNG, GIF (the first frame), BMP. 
 * The image file size should be no larger than 6 MB.
 * The minimum detectable face size is 36 x 36 pixels in an image that is no larger than 1920 x 1080 pixels. Images with larger than 1920 x 1080 pixels have a proportionally larger minimum face size. Reducing the face size might cause some faces not to be detected, even if they are larger than the minimum detectable face size.
 * The maximum detectable face size is 4096 x 4096 pixels.
 * Faces outside the size range of 36 x 36 to 4096 x 4096 pixels will not be detected.
-* Some faces might not be detected because of technical challenges. Extreme face angles (head pose) or face occlusion (objects such as sunglasses or hands that block part of the face) can affect detection. Frontal and near-frontal faces give the best results.
+* Some faces might not be recognized because of technical challenges, such as:
+  * Images with extreme lighting, for example, severe backlighting.
+  * Obstructions that block one or both eyes.
+  * Differences in hair type or facial hair.
+  * Changes in facial appearance because of age.
+  * Extreme facial expressions.
 
-Input data with orientation information:
-* Some input images with JPEG format might contain orientation information in Exchangeable image file format (Exif) metadata. If Exif orientation is available, images will be automatically rotated to the correct orientation before sending for face detection. The face rectangle, landmarks, and head pose for each detected face will be estimated based on the rotated image.
-* To properly display the face rectangle and landmarks, you need to make sure the image is rotated correctly. Most of image visualization tools will auto-rotate the image according to its Exif orientation by default. For other tools, you might need to apply the rotation using your own code. The following examples show a face rectangle on a rotated image (left) and a non-rotated image (right).
+### Input data with orientation information:
 
-![Two face images with/without rotation](../Images/image-rotation.png)
+Some input images with JPEG format might contain orientation information in Exchangeable image file format (Exif) metadata. If Exif orientation is available, images will be automatically rotated to the correct orientation before sending for face detection. The face rectangle, landmarks, and head pose for each detected face will be estimated based on the rotated image.
+
+To properly display the face rectangle and landmarks, you need to make sure the image is rotated correctly. Most of image visualization tools will auto-rotate the image according to its Exif orientation by default. For other tools, you might need to apply the rotation using your own code. The following examples show a face rectangle on a rotated image (left) and a non-rotated image (right).
+
+![Two face images with and without rotation](../Images/image-rotation.png)
+
+### Video input
 
 If you're detecting faces from a video feed, you may be able to improve performance by adjusting certain settings on your video camera:
 
diff --git a/articles/cognitive-services/Face/concepts/face-recognition.md b/articles/cognitive-services/Face/concepts/face-recognition.md
diff --git a/articles/cognitive-services/Face/index.yml b/articles/cognitive-services/Face/index.yml
