typos

Author: Juliako

articles/azure-video-indexer/audio-effects-detection-overview.md

@@ -24,7 +24,7 @@ Review [transparency note overview](/legal/azure-video-indexer/transparency-note
 
 This article discusses audio effects detection and the key considerations for making use of this technology responsibly. There are many things you need to consider when deciding how to use and implement an AI-powered feature: 
 
-* Will this feature perform well in my scenario? Before deploying audio effects detection into your scenario, test how it performs using real-life data and make sure it can deliver the accuracy you need. 
+* Does this feature perform well in my scenario? Before deploying audio effects detection into your scenario, test how it performs using real-life data and make sure it can deliver the accuracy you need. 
 * Are we equipped to identify and respond to errors? AI-powered products and features won't be 100% accurate, so consider how you'll identify and respond to any errors that may occur. 
 
 ## View the insight
@@ -79,7 +79,7 @@ During the audio effects detection procedure, audio in a media file is processed
 |Component|Definition|
 |---|---|
 |Source file |	The user uploads the source file for indexing. |
-|Segmentation|  	The audio is analyzed, non-speech audio is identified and then split into short overlapping internals. |
+|Segmentation|  	The audio is analyzed, nonspeech audio is identified and then split into short overlapping internals. |
 |Classification| 	An AI process analyzes each segment and classifies its contents into event categories such as crowd reaction or laughter. A probability list is then created for each event category according to department-specific rules. |
 |Confidence level|	The estimated confidence level of each audio effect is calculated as a range of 0 to 1. The confidence score represents the certainty in the accuracy of the result. For example, an 82% certainty is represented as an 0.82 score.|
 
@@ -91,7 +91,7 @@ During the audio effects detection procedure, audio in a media file is processed
 
 ## Considerations and limitations when choosing a use case 
 
-- Avoid use of very short or low-quality audio, audio effects detection provides probabilistic and partial data on detected nonspeech audio events. For accuracy, audio effects detection requires at least 2 seconds of clear nonspeech audio. Voice commands or singing aren't supported.   
+- Avoid use of short or low-quality audio, audio effects detection provides probabilistic and partial data on detected nonspeech audio events. For accuracy, audio effects detection requires at least 2 seconds of clear nonspeech audio. Voice commands or singing aren't supported.   
 - Avoid use of audio with loud background music or music with repetitive and/or linearly scanned frequency, audio effects detection is designed for nonspeech audio only and therefore can't classify events in loud music. Music with repetitive and/or linearly scanned frequency many be incorrectly classified as an alarm or siren. 
 - Carefully consider the methods of usage in law enforcement and similar institutions, to promote more accurate probabilistic data, carefully review the following: 
 
@@ -110,7 +110,7 @@ When used responsibly and carefully, Azure Video Indexer is a valuable tool for
 - Always respect an individual’s right to privacy, and only ingest audio for lawful and justifiable purposes.   
 - Don't purposely disclose inappropriate audio of young children or family members of celebrities or other content that may be detrimental or pose a threat to an individual’s personal freedom.   
 - Commit to respecting and promoting human rights in the design and deployment of your analyzed audio.   
-- When using 3rd party materials, be aware of any existing copyrights or permissions required before distributing content derived from them.  
+- When using third party materials, be aware of any existing copyrights or permissions required before distributing content derived from them.  
 - Always seek legal advice when using audio from unknown sources.  
 - Be aware of any applicable laws or regulations that exist in your area regarding processing, analyzing, and sharing audio containing people.  
 - Keep a human in the loop. Don't use any solution as a replacement for human oversight and decision-making.   

articles/azure-video-indexer/face-detection.md

@@ -36,8 +36,8 @@ This article discusses faces detection and the key considerations for making use
 |---|---|
 |Insight  |The information and knowledge derived from the processing and analysis of video and audio files that generate different types of insights and can include detected objects, people, faces, animated characters, keyframes and translations or transcriptions. |
 |Face recognition  |The analysis of images to identify the faces that appear in the images. This process is implemented via the Azure Cognitive Services Face API. |
-|Template |Enrolled images of people are converted to templates, which are then used for facial recognition. Machine-interpretable features are extracted from one or more images of an individual to create that individual’s template. The enrolment or probe images aren't stored by Face API and the original images can't be reconstructed based on a template. Template quality is a key determinant on the accuracy of your results. |
-|Enrolment |The process of enrolling images of individuals for template creation so they can be recognized. When a person is enrolled to a verification system used for authentication, their template is also associated with a primary identifier2 that is used to determine which template to compare with the probe template. High-quality images and images representing natural variations in how a person looks (for instance wearing glasses, not wearing glasses) generate high-quality enrolment templates. |
+|Template |Enrolled images of people are converted to templates, which are then used for facial recognition. Machine-interpretable features are extracted from one or more images of an individual to create that individual’s template. The enrollment or probe images aren't stored by Face API and the original images can't be reconstructed based on a template. Template quality is a key determinant on the accuracy of your results. |
+|Enrolment |The process of enrolling images of individuals for template creation so they can be recognized. When a person is enrolled to a verification system used for authentication, their template is also associated with a primary identifier2 that is used to determine which template to compare with the probe template. High-quality images and images representing natural variations in how a person looks (for instance wearing glasses, not wearing glasses) generate high-quality enrollment templates. |
 |Deep search  |The ability to retrieve only relevant video and audio files from a video library by searching for specific terms within the extracted insights.|
 
 ## View the insight
@@ -49,7 +49,7 @@ To see the instances on the website, do the following:
 
 To see face detection insight in the JSON file, do the following:  
 
-1. Click Download -> Insights (JSON).  
+1. Select Download -> Insights (JSON).  
 1. Copy the `faces` element, under `insights`, and paste it into your JSON viewer. 
 
     ```json
@@ -115,15 +115,15 @@ During the Faces Detection procedure, images in a media file are processed, as f
 ## Considerations when choosing a use case 
 
 * Carefully consider the accuracy of the results, to promote more accurate detections, check the quality of the video, low quality video might impact the detected insights.   
-* Carefully consider when using for law enforcement. People might not be detected if they are small, sitting, crouching, or obstructed by objects or other people. To ensure fair and high-quality decisions, combine face detection-based automation with human oversight. 
-* Do not use face detection for decisions that may have serious adverse impacts. Decisions based on incorrect output could have serious adverse impacts. Additionally, it is advisable to include human review of decisions that have the potential for serious impacts on individuals. 
+* Carefully consider when using for law enforcement. People might not be detected if they're small, sitting, crouching, or obstructed by objects or other people. To ensure fair and high-quality decisions, combine face detection-based automation with human oversight. 
+* Don't use face detection for decisions that may have serious adverse impacts. Decisions based on incorrect output could have serious adverse impacts. Additionally, it's advisable to include human review of decisions that have the potential for serious impacts on individuals. 
 
 When used responsibly and carefully face detection is a valuable tool for many industries. To respect the privacy and safety of others, and to comply with local and global regulations, we recommend the following:   
 
 * Always respect an individual’s right to privacy, and only ingest videos for lawful and justifiable purposes.   
 * Don't purposely disclose inappropriate content about young children or family members of celebrities or other content that may be detrimental or pose a threat to an individual’s personal freedom.   
 * Commit to respecting and promoting human rights in the design and deployment of your analyzed media.   
-* When using 3rd party materials, be aware of any existing copyrights or permissions required before distributing content derived from them.  
+* When using third party materials, be aware of any existing copyrights or permissions required before distributing content derived from them.  
 * Always seek legal advice when using content from unknown sources.  
 * Always obtain appropriate legal and professional advice to ensure that your uploaded videos are secured and have adequate controls to preserve the integrity of your content and to prevent unauthorized access.     
 * Provide a feedback channel that allows users and individuals to report issues with the service.   

articles/azure-video-indexer/labels-identification.md

@@ -12,7 +12,7 @@ ms.topic: article
 
 # Labels identification  
 
-Labels identification is an Azure Video Indexer AI feature that identifies visual objects like sunglasses or actions like swimming, appearing in the video footage of a media file. There are many labels identification categories and once extracted, labels identification instances are displayed in the Insights tab and can be translated into over 50 languages. Clicking a Label opens the instance in the media file, click Play Previous or Play Next to see more instances. 
+Labels identification is an Azure Video Indexer AI feature that identifies visual objects like sunglasses or actions like swimming, appearing in the video footage of a media file. There are many labels identification categories and once extracted, labels identification instances are displayed in the Insights tab and can be translated into over 50 languages. Clicking a Label opens the instance in the media file, select Play Previous or Play Next to see more instances. 
 
 ## Prerequisites  
 
@@ -22,8 +22,8 @@ Review [Transparency Note overview](/legal/azure-video-indexer/transparency-note
 
 This article discusses labels identification and the key considerations for making use of this technology responsibly. There are many things you need to consider when deciding how to use and implement an AI-powered feature:
 
-- Will this feature perform well in my scenario? Before deploying labels identification into your scenario, test how it performs using real-life data and make sure it can deliver the accuracy you need.
-- Are we equipped to identify and respond to errors? AI-powered products and features will not be 100% accurate, so consider how you will identify and respond to any errors that may occur.
+- Does this feature perform well in my scenario? Before deploying labels identification into your scenario, test how it performs using real-life data and make sure it can deliver the accuracy you need.
+- Are we equipped to identify and respond to errors? AI-powered products and features won't be 100% accurate, so consider how you'll identify and respond to any errors that may occur.
 
 ## View the insight
 
@@ -83,8 +83,6 @@ To display labels identification insights in a JSON file, do the following:
 
 To download the JSON file via the API, [Azure Video Indexer developer portal](https://api-portal.videoindexer.ai/). 
 
-<!-- For more information, see [Azure Video Indexer Labels]().-->
-
 ## Labels components 
 
 During the Labels procedure, objects in a media file are processed, as follows:

articles/azure-video-indexer/ocr.md

@@ -22,7 +22,7 @@ Review [transparency note overview](/legal/azure-video-indexer/transparency-note
 
 ## General principles 
 
-This article discusses optical character recognition (OCR) and the key considerations for making use of this technology responsibly. There are a number of things you need to consider when deciding how to use and implement an AI-powered feature: 
+This article discusses optical character recognition (OCR) and the key considerations for making use of this technology responsibly. There are many things you need to consider when deciding how to use and implement an AI-powered feature: 
 
 - Will this feature perform well in my scenario? Before deploying OCR into your scenario, test how it performs using real-life data and make sure it can deliver the accuracy you need. 
 - Are we equipped to identify and respond to errors? AI-powered products and features won't be 100% accurate, so consider how you'll identify and respond to any errors that may occur. 
@@ -34,13 +34,13 @@ When working on the website the insights are displayed in the **Timeline** tab.
 To see the instances on the website, do the following: 
 
 1. Go to View and check OCR.  
-1. Click Timeline to display the extracted text. 
+1. Select Timeline to display the extracted text. 
 
-Insights can also be generated in a categorized list in a JSON file which includes the ID, language, text together with each instance’s confidence score.  
+Insights can also be generated in a categorized list in a JSON file that includes the ID, language, text together with each instance’s confidence score.  
 
 To see  the insights in a JSON file, do the following: 
 
-1. Click Download -> Insight (JSON).  
+1. Select Download -> Insight (JSON).  
 1. Copy the `ocr` element, under `insights`, and paste it into your online JSON viewer. 
 
     ```json
@@ -95,7 +95,7 @@ To download the JSON file via the API, use the [Azure Video Indexer developer po
 
 ## OCR components 
 
-During the OCR procedure, text images in a media file is processed, as follows:  
+During the OCR procedure, text images in a media file are processed, as follows:  
 
 |Component|Definition|
 |---|---|
@@ -111,22 +111,22 @@ For more information, see [OCR technology](/azure/cognitive-services/computer-
 - Deep searching media footage for images with signposts, street names or car license plates, for example, in law enforcement. 
 - Extracting text from images in media files and then translating it into multiple languages in labels for accessibility, for example in media or entertainment. 
 - Detecting brand names in images and tagging them for translation purposes, for example in advertising and branding. 
-- Extracting text in images which is then automatically tagged and categorized for accessibility and future usage, for example to generate content at a news agency. 
+- Extracting text in images that is then automatically tagged and categorized for accessibility and future usage, for example to generate content at a news agency. 
 - Extracting text in warnings in online instructions and then translating the text to comply with local standards, for example, e-learning instructions for using equipment.     
 
 ## Considerations and limitations when choosing a use case 
 
 - Carefully consider the accuracy of the results, to promote more accurate detections, check the quality of the image, low quality images might impact the detected insights.  
 - Carefully consider when using for law enforcement that OCR can potentially misread or not detect parts of the text. To ensure fair and high-quality decisions, combine OCR-based automation with human oversight. 
-- When extracting handwritten text, avoid using the OCR results of signatures which are hard to read for both humans and machines. A better way to use OCR is to use it for detecting the presence of a signature for further analysis. 
+- When extracting handwritten text, avoid using the OCR results of signatures that are hard to read for both humans and machines. A better way to use OCR is to use it for detecting the presence of a signature for further analysis. 
 - Don't use OCR for decisions that may have serious adverse impacts. Machine learning models that extract text can result in undetected or incorrect text output. Decisions based on incorrect output could have serious adverse impacts. Additionally, it's advisable to include human review of decisions that have the potential for serious impacts on individuals. 
 
 When used responsibly and carefully, Azure Video Indexer is a valuable tool for many industries. To respect the privacy and safety of others, and to comply with local and global regulations, we recommend the following:   
 
 - Always respect an individual’s right to privacy, and only ingest videos for lawful and justifiable purposes.   
-- Do not purposely disclose inappropriate content about young children or family members of celebrities or other content that may be detrimental or pose a threat to an individual’s personal freedom.   
+- Don't purposely disclose inappropriate content about young children or family members of celebrities or other content that may be detrimental or pose a threat to an individual’s personal freedom.   
 - Commit to respecting and promoting human rights in the design and deployment of your analyzed media.   
-- When using 3rd party materials, be aware of any existing copyrights or permissions required before distributing content derived from them.  
+- When using third party materials, be aware of any existing copyrights or permissions required before distributing content derived from them.  
 - Always seek legal advice when using content from unknown sources.  
 - Always obtain appropriate legal and professional advice to ensure that your uploaded videos are secured and have adequate controls to preserve the integrity of your content and to prevent unauthorized access.     
 - Provide a feedback channel that allows users and individuals to report issues with the service.