Merge pull request #203790 from HeidiSteen/heidist-fresh

minor edits

Author: PRMerger7

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

@@ -35,28 +35,28 @@ Optionally, you can define projections to accept image-analyzed output into a [k
 
 ## Set up source files
 
-Image processing is indexer-driven, which means that the raw inputs must be a supported file type (as determined by the skills you choose) from a [supported data source](search-indexer-overview.md#supported-data-sources). 
+Image processing is indexer-driven, which means that the raw inputs must be in a [supported data source](search-indexer-overview.md#supported-data-sources). 
 
 + Image analysis supports JPEG, PNG, GIF, and BMP
 + OCR supports JPEG, PNG, BMP, and TIF
 
 Images are either standalone binary files or embedded in documents (PDF, RTF, and Microsoft application files). A maximum of 1000 images will be extracted from a given document. If there are more than 1000 images in a document, the first 1000 will be extracted and a warning will be generated.
 
-Azure Blob Storage is the most frequently used storage for image processing in Cognitive Search. There are three main tasks related to retrieving images from the source:
+Azure Blob Storage is the most frequently used storage for image processing in Cognitive Search. There are three main tasks related to retrieving images from a blob container:
 
-+ Access rights on the container. If you're using a full access connection string that includes a key, the key gives you access to the content. Alternatively, you can [authenticate using Azure Active Directory (Azure AD)](search-howto-managed-identities-data-sources.md) or [connect as a trusted service](search-indexer-howto-access-trusted-service-exception.md).
++ Enable access to content in the container. If you're using a full access connection string that includes a key, the key gives you permission to the content. Alternatively, you can [authenticate using Azure Active Directory (Azure AD)](search-howto-managed-identities-data-sources.md) or [connect as a trusted service](search-indexer-howto-access-trusted-service-exception.md).
 
 + [Create a data source](search-howto-indexing-azure-blob-storage.md) of type "azureblob" that connects to the blob container storing your files.
 
-+ Optionally, [set file type criteria](search-blob-storage-integration.md#PartsOfBlobToIndex) if the workload targets a specific file type. Blob indexer configuration includes file inclusion and exclusion settings. You can filter out files you don't want.
++ Review [service tier limits](search-limits-quotas-capacity.md) to make sure that your source data is under maximum size and quantity limits for indexers and enrichment.
 
 <a name="get-normalized-images"></a>
 
 ## Configure indexers for image processing
 
-Image extraction is the first step of indexer processing. Extracted images are queued for image processing. Extracted text is queued for text processing, if applicable.
+Extracting images from the source content files is the first step of indexer processing. Extracted images are queued for image processing. Extracted text is queued for text processing, if applicable.
 
-Image processing requires image normalization to make images more uniform for downstream processing. This step occurs automatically and is internal to indexer processing. As a developer, you enable image normalization by setting the `"imageAction"` parameter in indexer configuration.
+Image processing requires image normalization to make images more uniform for downstream processing. This second step occurs automatically and is internal to indexer processing. As a developer, you enable image normalization by setting the `"imageAction"` parameter in indexer configuration.
 
 Image normalization includes the following operations:
 
@@ -90,7 +90,7 @@ Metadata adjustments are captured in a complex type created for each image. You
 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.
- 
+
    + `"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 same as if you had set "generateNormalizedImages".
 
 1. Optionally, adjust the width or height of the generated normalized images:
@@ -101,6 +101,19 @@ Metadata adjustments are captured in a complex type created for each image. You
 
    The default of 2000 pixels for the normalized images maximum width and height is based on the maximum sizes supported by the [OCR skill](cognitive-search-skill-ocr.md) and the [image analysis skill](cognitive-search-skill-image-analysis.md). The [OCR skill](cognitive-search-skill-ocr.md) supports a maximum width and height of 4200 for non-English languages, and 10000 for English.  If you increase the maximum limits, processing could fail on larger images depending on your skillset definition and the language of the documents. 
 
++ Optionally, [set file type criteria](search-blob-storage-integration.md#PartsOfBlobToIndex) if the workload targets a specific file type. Blob indexer configuration includes file inclusion and exclusion settings. You can filter out files you don't want.
+
+  ```json
+  {
+    "parameters" : { 
+        "configuration" : { 
+            "indexedFileNameExtensions" : ".pdf, .docx",
+            "excludedFileNameExtensions" : ".png, .jpeg" 
+        } 
+    }
+  }
+  ```
+
 ### About normalized images
 
 When "imageAction" is set to a value other than "none", the new *normalized_images* field will contain an array of images. Each  image is a complex type that has the following members:
@@ -143,6 +156,8 @@ This section supplements the [skill reference](cognitive-search-predefined-skill
 
 1. If necessary, [include multi-service key](cognitive-search-attach-cognitive-services.md) in the Cognitive Services property of the skillset. Cognitive Search makes calls to a billable Azure Cognitive Services resource for OCR and image analysis for transactions that exceed the free limit (20 per indexer per day). Cognitive Services must be in the same region as your search service.
 
+1. If original images are embedded in PDF or application files like PPTX or DOCX, you'll need to add a Text Merge skill if you want image output and text output together. Working with embedded images is discussed further on in this article.
+
 Once the basic framework of your skillset is created and Cognitive Services is configured, you can focus on each individual image skill, defining inputs and source context, and mapping outputs to fields in either an index or knowledge store.
 
 > [!NOTE]
@@ -391,7 +406,7 @@ Image analysis output is illustrated in the JSON below (search result). The skil
 
 When the images you want to process are embedded in other files, such as PDF or DOCX, the enrichment pipeline will extract just the images and then pass them to OCR or image analysis for processing. Separation of image from text content occurs during the document cracking phase, and once the images are separated, they remain separate unless you explicitly merge the processed output back into the source text. 
 
-[**Text Merge**](cognitive-search-skill-textmerger.md) is used to put image processing output back into the document. Although Text Merge is not a hard requirement, it's frequently invoked so that image output (OCR text, OCR layoutText, image tags, image captions) can be reintroduced into the document at the same location where the image was found. Essentially, the goal is to replace an embedded binary image with an in-place text equivalent.
+[**Text Merge**](cognitive-search-skill-textmerger.md) is used to put image processing output back into the document. Although Text Merge is not a hard requirement, it's frequently invoked so that image output (OCR text, OCR layoutText, image tags, image captions) can be reintroduced into the document. Depending on the skill, the image output replaces an embedded binary image with an in-place text equivalent. Image Analysis output can be merged at image location. OCR output always appears at the end of each page.
 
 The following workflow outlines the process of image extraction, analysis, merging, and how to extend the pipeline to push image-processed output into other text-based skills such as Entity Recognition or Text Translation.
 

articles/search/cognitive-search-concept-intro.md

@@ -17,7 +17,7 @@ ms.custom: references_regions
 
 Because Azure Cognitive Search is a full text search solution, the purpose of AI enrichment is to improve the utility of your content in search-related scenarios:
 
-+ Machine translation and language detection, in support of multi-lingual search
++ Translation and language detection for multi-lingual search
 + Entity recognition extracts people, places, and other entities from large chunks of text
 + Key phrase extraction identifies and then outputs important terms
 + Optical Character Recognition (OCR) recognizes printed and handwritten text in binary files

articles/search/cognitive-search-skill-ocr.md

@@ -58,9 +58,7 @@ In previous versions, there was a parameter called "textExtractionAlgorithm" to
 | `text`             | Plain text extracted from the image.   |
 | `layoutText`    | Complex type that describes the extracted text and the location where the text was found.|
 
-
-The OCR skill always extracts images at the end of each page. This is by design.
-
+If you call OCR on images embedded in PDFs or other application files, the OCR output will be located at the bottom of the page, after any text that was extracted and processed.
 
 ## Sample definition
 

articles/search/search-what-is-azure-search.md

@@ -15,12 +15,10 @@ ms.custom: contperf-fy21q1
 
 Azure Cognitive Search ([formerly known as "Azure Search"](whats-new.md#new-service-name)) is a cloud search service that gives developers infrastructure, APIs, and tools for building a rich search experience over private, heterogeneous content in web, mobile, and enterprise applications.
 
-Search is foundational to any app that surfaces text content to users, with common scenarios including catalog or document search, online retail, or data exploration over proprietary content.
-
-When you create a search service, you'll work with the following capabilities:
+Search is foundational to any app that surfaces text content to users, with common scenarios including catalog or document search, online retail, or data exploration over proprietary content. When you create a search service, you'll work with the following capabilities:
 
 + A search engine for full text search over a search index containing your user-owned content
-+ Rich indexing, with [text analysis](search-analyzers.md) and [optional AI enrichment](cognitive-search-concept-intro.md) for advanced content extraction and transformation
++ Rich indexing, with [text analysis](search-analyzers.md) and [optional AI enrichment](cognitive-search-concept-intro.md) for content extraction and transformation
 + Rich query syntax that supplements free text search with filters, autocomplete, regex, geo-search and more
 + Programmability through REST APIs and client libraries in Azure SDKs for .NET, Python, Java, and JavaScript
 + Azure integration at the data layer, machine learning layer, and AI (Cognitive Services)