Fix issue

Author: cdpark

articles/search/cognitive-search-concept-image-scenarios.md

@@ -78,17 +78,17 @@ Metadata adjustments are captured in a complex type created for each image. You
     }
     ```
 
-1. Set `dataToExtract` to *contentAndMetadata* (required).
+1. Set `dataToExtract` to `contentAndMetadata` (required).
 
 1. Verify that the `parsingMode` is set to *default* (required).
 
    This parameter determines the granularity of search documents created in the index. The default mode sets up a one-to-one correspondence so that one blob results in one search document. If documents are large, or if skills require smaller chunks of text, you can add the Text Split skill that subdivides a document into paging for processing purposes. But for search scenarios, one blob per document is required if enrichment includes image processing.
 
 1. Set `imageAction` to enable the `normalized_images` node in an enrichment tree (required):
 
-   + *generateNormalizedImages* to generate an array of normalized images as part of document cracking.
+   + `generateNormalizedImages` to generate an array of normalized images as part of document cracking.
 
-   + *generateNormalizedImagePerPage* (applies to PDF only) to generate an array of normalized images where each page in the PDF is rendered to one output image. For non-PDF files, the behavior of this parameter is similar as if you had set *generateNormalizedImages*. However, setting *generateNormalizedImagePerPage* can make indexing operation less performant by design (especially for large documents) since several images would have to be generated.
+   + `generateNormalizedImagePerPage` (applies to PDF only) to generate an array of normalized images where each page in the PDF is rendered to one output image. For non-PDF files, the behavior of this parameter is similar as if you had set `generateNormalizedImages`. However, setting `generateNormalizedImagePerPage` can make indexing operation less performant by design (especially for large documents) since several images would have to be generated.
 
 1. Optionally, adjust the width or height of the generated normalized images:
 
@@ -123,7 +123,7 @@ When `imageAction` is set to a value other than *none*, the new `normalized_imag
 | originalWidth      | The original width of the image before normalization. |
 | originalHeight      | The original height of the image before normalization. |
 | rotationFromOriginal |  Counter-clockwise rotation in degrees that occurred to create the normalized image. A value between 0 degrees and 360 degrees. This step reads the metadata from the image that is generated by a camera or scanner. Usually a multiple of 90 degrees. |
-| contentOffset | The character offset within the content field where the image was extracted from. This field is only applicable for files with embedded images. The *contentOffset* for images extracted from PDF documents is always at the end of the text on the page it was extracted from in the document. This means images appear after all text on that page, regardless of the original location of the image in the page. |
+| contentOffset | The character offset within the content field where the image was extracted from. This field is only applicable for files with embedded images. The `contentOffset` for images extracted from PDF documents is always at the end of the text on the page it was extracted from in the document. This means images appear after all text on that page, regardless of the original location of the image in the page. |
 | pageNumber | If the image was extracted or rendered from a PDF, this field contains the page number in the PDF it was extracted or rendered from, starting from 1. If the image isn't from a PDF, this field is 0.  |
 
  Sample value of `normalized_images`:
@@ -226,7 +226,7 @@ In a skillset, Image Analysis and OCR skill output is always text. Output text i
 
 1. [Create or update a search index](/rest/api/searchservice/indexes/create-or-update) to add fields to accept the skill outputs. 
 
-   In the following fields collection example, *content* is blob content. *Metadata_storage_name* contains the name of the file (make `retrievable` is set to *true*). *Metadata_storage_path* is the unique path of the blob and is the default document key. *Merged_content* is output from Text Merge (useful when images are embedded). 
+   In the following fields collection example, *content* is blob content. *Metadata_storage_name* contains the name of the file (set `retrievable` to *true*). *Metadata_storage_path* is the unique path of the blob and is the default document key. *Merged_content* is output from Text Merge (useful when images are embedded). 
 
     *Text* and *layoutText* are OCR skill outputs and must be a string collection in order to the capture all of the OCR-generated output for the entire document.
 
@@ -411,17 +411,17 @@ The following workflow outlines the process of image extraction, analysis, mergi
 
 1. Images in the queue are [normalized](#get-normalized-images) and passed into enriched documents as a [document/normalized_images](#get-normalized-images) node.
 
-1. Image enrichments execute, using *"/document/normalized_images"* as input.
+1. Image enrichments execute, using `"/document/normalized_images"` as input.
 
 1. Image outputs are passed into the enriched document tree, with each output as a separate node. Outputs vary by skill (text and layoutText for OCR; tags and captions for Image Analysis).
 
 1. Optional but recommended if you want search documents to include both text and image-origin text together, [Text Merge](cognitive-search-skill-textmerger.md) runs, combining the text representation of those images with the raw text extracted from the file. Text chunks are consolidated into a single large string, where the text is inserted first in the string and then the OCR text output or image tags and captions.
 
-   The output of Text Merge is now the definitive text to analyze for any downstream skills that perform text processing. For example, if your skillset includes both OCR and Entity Recognition, the input to Entity Recognition should be *"document/merged_text"* (the targetName of the Text Merge skill output).
+   The output of Text Merge is now the definitive text to analyze for any downstream skills that perform text processing. For example, if your skillset includes both OCR and Entity Recognition, the input to Entity Recognition should be `"document/merged_text"` (the targetName of the Text Merge skill output).
 
 1. After all skills have executed, the enriched document is complete. In the last step, indexers refer to [output field mappings](#output-field-mappings) to send enriched content to individual fields in the search index.
 
-The following example skillset creates a *merged_text* field containing the original text of your document with embedded OCRed text in place of embedded images. It also includes an Entity Recognition skill that uses *merged_text* as input.
+The following example skillset creates a `merged_text` field containing the original text of your document with embedded OCRed text in place of embedded images. It also includes an Entity Recognition skill that uses `merged_text` as input.
 
 ### Request body syntax
 
@@ -492,7 +492,7 @@ The following example skillset creates a *merged_text* field containing the orig
 }
 ```
 
-Now that you have a *merged_text* field, you can map it as a searchable field in your indexer definition. All of the content of your files, including the text of the images, will be searchable.
+Now that you have a `merged_text` field, you can map it as a searchable field in your indexer definition. All of the content of your files, including the text of the images, will be searchable.
 
 ## Scenario: Visualize bounding boxes
 

articles/search/cognitive-search-output-field-mapping.md

@@ -13,7 +13,7 @@ ms.date: 10/15/2024
 
 # Map enriched output to fields in a search index in Azure AI Search
 
-:::image type="content" source="media/cognitive-search-output-field-mapping/indexer-stages-output-field-mapping.pn" alt-text="Diagram of the Indexer Stages with Output Field Mappings highlighted.":::
+:::image type="content" source="media/cognitive-search-output-field-mapping/indexer-stages-output-field-mapping.png" alt-text="Diagram of the Indexer Stages with Output Field Mappings highlighted.":::
 
 This article explains how to set up *output field mappings*, defining a data path between in-memory data generated during [skillset processing](cognitive-search-concept-intro.md), and target fields in a search index. During indexer execution, skills-generated information exists in memory only. To persist this information in a search index, you need to tell the indexer where to send the data.
 

articles/search/cognitive-search-quickstart-blob.md

@@ -15,7 +15,7 @@ ms.date: 10/15/2024
 
 # Quickstart: Create a skillset in the Azure portal
 
-In this quickstart, you learn how a skillset in Azure AI Search adds Optical Character Recognition (OCR), image analysis, language detection, text translation, and entity recognition to generate text-searchable content in a search index. 
+In this quickstart, you learn how a skillset in Azure AI Search adds optical character recognition (OCR), image analysis, language detection, text translation, and entity recognition to generate text-searchable content in a search index.
 
 You can run the **Import data** wizard in the Azure portal to apply skills that create and transform textual content during indexing. Input is your raw data, usually blobs in Azure Storage. Output is a searchable index containing AI-generated image text, captions, and entities. Generated content is queryable in the portal using [**Search explorer**](search-explorer.md).
 
@@ -27,7 +27,7 @@ To prepare, you create a few resources and upload sample files before running th
 
 + Create an [Azure AI Search service](search-create-service-portal.md) or [find an existing service](https://portal.azure.com/#blade/HubsExtension/BrowseResourceBlade/resourceType/Microsoft.Search%2FsearchServices). You can use a free service for this quickstart. 
 
-+ Azure Storage account with Blob Storage.
++ An Azure Storage account with Azure Blob Storage.
 
 > [!NOTE]
 > This quickstart uses [Azure AI services](https://azure.microsoft.com/services/cognitive-services/) for the AI transformations. Because the workload is so small, Azure AI services is tapped behind the scenes for free processing for up to 20 transactions. You can complete this exercise without having to create an Azure AI multi-service resource.
@@ -190,7 +190,7 @@ If you used a free service, remember that you're limited to three indexes, index
 
 ## Next step
 
-You can create skillsets using the portal, .NET SDK, or REST API. To further your knowledge, try the REST API using a REST client and more sample data.
+You can create skillsets using the portal, .NET SDK, or REST API. To further your knowledge, try the REST API by using a REST client and more sample data.
 
 > [!div class="nextstepaction"]
-> [Tutorial: Extract text and structure from JSON blobs using REST APIs ](cognitive-search-tutorial-blob.md)
+> [Tutorial: Use skillsets to generate searchable content in Azure AI Search](cognitive-search-tutorial-blob.md)