acrolinx

Author: laujan

articles/ai-services/document-intelligence/concept-contract.md

@@ -31,7 +31,7 @@ The Document Intelligence contract model uses powerful Optical Character Recogni
 
 ## Automated contract processing
 
-Automated contract processing is the process of extracting key contract fields from documents. Historically, the contract analysis process has been done manually and, hence, very time consuming. Accurate extraction of key data from contracts is typically the first and one of the most critical steps in the contract automation process.
+Automated contract processing is the process of extracting key contract fields from documents. Historically, the contract analysis process is achieved manually and, hence, very time consuming. Accurate extraction of key data from contracts is typically the first and one of the most critical steps in the contract automation process.
 
 ## Development options
 

articles/ai-services/document-intelligence/concept-custom.md

@@ -62,7 +62,7 @@ The custom template or custom form model relies on a consistent visual template
 
 Your training set consists of structured documents where the formatting and layout are static and constant from one document instance to the next. Custom template models support key-value pairs, selection marks, tables, signature fields, and regions. Template models and can be trained on documents in any of the [supported languages](language-support.md). For more information, *see* [custom template models](concept-custom-template.md).
 
-If the language of your documents and extraction scenarios supports custom neural models, it's recommended that you use custom neural models over template models for higher accuracy.
+If the language of your documents and extraction scenarios supports custom neural models, we recommend that you use custom neural models over template models for higher accuracy.
 
 > [!TIP]
 >
@@ -104,7 +104,7 @@ If the language of your documents and extraction scenarios supports custom neura
 
 ### Build mode
 
-The build custom model operation has added support for the *template* and *neural* custom models. Previous versions of the REST API and SDKs only supported a single build mode that is now known as the *template* mode.
+The build custom model operation adds support for the *template* and *neural* custom models. Previous versions of the REST API and SDKs only supported a single build mode that is now known as the *template* mode.
 
 * Template models only accept documents that have the same basic page structure—a uniform visual appearance—or the same relative positioning of elements within the document.
 

articles/ai-services/document-intelligence/concept-general-document.md

@@ -23,7 +23,7 @@ ms.author: lajanuar
 
 | Feature   | version| Model ID |
 |----------  |---------|--------|
-|Layout model with the optional query string parameter **`features=keyValuePairs`** enabled.|&bullet; v4:2023-10-31-preview</br>&bullet; v3.1:2023-07-31 (GA) |**`prebuilt-layout`**|
+|`Layout` model with the optional query string parameter **`features=keyValuePairs`** enabled.|&bullet; v4:2023-10-31-preview</br>&bullet; v3.1:2023-07-31 (GA) |**`prebuilt-layout`**|
 |General document model|&bullet; v3.1:2023-07-31 (GA)</br>&bullet; v3.0:2022-08-31 (GA)</br>&bullet; v2.1 (GA)|**`prebuilt-document`**|
 :::moniker-end
 

articles/ai-services/document-intelligence/concept-layout.md

@@ -163,7 +163,7 @@ See how data, including text, tables, table headers, selection marks, and struct
 
     * Select the **Fetch** button.
 
-1. Select **Run Layout**. The Document Intelligence Sample Labeling tool calls the Analyze Layout API and analyze the document.
+1. Select **Run Layout**. The Document Intelligence Sample Labeling tool calls the `Analyze Layout` API and analyze the document.
 
     :::image type="content" source="media/fott-layout.png" alt-text="Screenshot of `Layout` dropdown window.":::
 
@@ -410,7 +410,7 @@ For large multi-page documents, use the `pages` query parameter to indicate spec
 
 ## The Get Analyze Layout Result operation
 
-The second step is to call the [Get Analyze Layout Result](https://westcentralus.dev.cognitive.microsoft.com/docs/services/form-recognizer-api-v2-1/operations/GetAnalyzeLayoutResult) operation. This operation takes as input the Result ID the Analyze Layout operation created. It returns a JSON response that contains a **status** field with the following possible values.
+The second step is to call the [Get Analyze Layout Result](https://westcentralus.dev.cognitive.microsoft.com/docs/services/form-recognizer-api-v2-1/operations/GetAnalyzeLayoutResult) operation. This operation takes as input the Result ID the `Analyze Layout` operation created. It returns a JSON response that contains a **status** field with the following possible values.
 
 |Field| Type | Possible values |
 |:-----|:----:|:----|

articles/ai-services/document-intelligence/concept-tax-document.md

@@ -160,13 +160,13 @@ The following are the fields extracted from a 1099-nec tax form in the JSON outp
 
 |Name| Type | Description | Example output |
 |:-----|:----|:----|:---:|
-| TaxYear | String | Tax Year extracted from Form 1099-NEC.| 2021 |
-| Payer | Object | An object that contains the payers's TIN, Name, Address, and PhoneNumber | |
-| Recipient | Object | An object that contains the recipient's TIN, Name, Address, and AccountNumber| |
-| Box1 |number|Box 1 extracted from Form 1099-NEC.| 123456 |
-| Box2 |boolean|Box 2 extracted from Form 1099-NEC.| true |
-| Box4 |number|Box 4 extracted from Form 1099-NEC.| 123456 |
-| StateTaxesWithheld |array| State Taxes Withheld extracted from Form 1099-NEC (boxes 5,6, and 7)| |
+| `TaxYear` | String | Tax Year extracted from Form 1099-NEC.| 2021 |
+| `Payer` | Object | An object that contains the payer's TIN, Name, Address, and PhoneNumber | |
+| `Recipient` | Object | An object that contains the recipient's TIN, Name, Address, and AccountNumber| |
+| `Box1` |number|Box 1 extracted from Form 1099-NEC.| 123456 |
+| `Box2` |boolean|Box 2 extracted from Form 1099-NEC.| true |
+| `Box4` |number|Box 4 extracted from Form 1099-NEC.| 123456 |
+| `StateTaxesWithheld` |array| State Taxes Withheld extracted from Form 1099-NEC (boxes 5, 6, and 7)| |
 
 The tax documents key-value pairs and line items extracted are in the `documentResults` section of the JSON output.
 

articles/ai-services/document-intelligence/faq.yml

@@ -129,11 +129,11 @@ sections:
            What is the confidence score and how is it calculated?
         answer: |
 
-           A confidence score indicates probability by measuring the degree of statistical certainty that the extracted result has been detected correctly.
+           A confidence score indicates probability by measuring the degree of statistical certainty that the extracted result is detected correctly.
 
            The confidence value range is a percentage between 0% (low) and 100% (high).
 
-           It's best to target a score of 80% or higher. For more sensitive cases, like financial or medical records, a score of close to 100% is recommended. You may also require human review.
+           It's best to target a score of 80% or higher. For more sensitive cases, like financial or medical records, a score of close to 100% is recommended. You can also require human review.
 
            See [Interpret and improve accuracy and confidence scores](concept-accuracy-confidence.md#confidence-scores)
 
@@ -159,7 +159,7 @@ sections:
           What is a bounding box?
         answer: |
 
-          A bounding box is an abstract rectangle that surrounds text elements on a document or form.  It's used as a reference point for object detection.
+          A bounding box is an abstract rectangle that surrounds text elements on a document or form and is used as a reference point for object detection.
 
           - The bounding box specifies position using an x and y coordinate plane presented in an array of four numerical pairs. Each pair represents a corner of the box in the following order: top-left, top-right, bottom-right, bottom-left.
 
@@ -277,7 +277,7 @@ sections:
 
           - When analyzing Microsoft Word and HTML files supported by only the Read model, pages are counted in blocks of 3,000 characters each. For example, if your document contains 7,000 characters, the two pages with 3,000 characters each and one page with 1,000 characters adds up to a total of three pages.
 
-          - In addition, when using the Read model, if your Microsoft Word, Excel, and PowerPoint pages have embedded images, each image is analyzed and counted as a page. Therefore, the total analyzed pages for Microsoft Office documents are equal to the sum of total text pages and total images analyzed. In the previous example if the document contains two embedded images, the total page count in the service output is three text pages plus two images equaling a total of five pages.
+          - When using the Read model, if your Microsoft Word, Excel, and PowerPoint pages with embedded images, each image is analyzed and counted as a page. Therefore, the total analyzed pages for Microsoft Office documents are equal to the sum of total text pages and total images analyzed. In the previous example if the document contains two embedded images, the total page count in the service output is three text pages plus two images equaling a total of five pages.
 
           - Training a custom model is always free with Document Intelligence. You’re only charged when a model is used to analyze a document.
 
@@ -314,7 +314,7 @@ sections:
         answer: |
           Document Intelligence is a multi-tenanted service where latency for similar documents is comparable but not always identical. The time to analyze a document depends on the size (for example, number of pages) and associated content on each page.
 
-          Latency is the amount of time it takes for an API server to handle and process an incoming request and deliver the outgoing response to the client. Occasional variability in latency and performance is inherent in any micro-service-based, stateless, asynchronous service that processes images and large documents at scale. While we're continuously scaling up the hardware and capacity and scaling capabilities, you may still see latency issues at run time.
+          Latency is the amount of time it takes for an API server to handle and process an incoming request and deliver the outgoing response to the client. Occasional variability in latency and performance is inherent in any micro-service-based, stateless, asynchronous service that processes images and large documents at scale. While we're continuously scaling up the hardware and capacity and scaling capabilities, you can still see latency issues at run time.
 
   - name: Custom models
     questions:
@@ -367,7 +367,7 @@ sections:
 
            - Model Compose is currently available only for custom models trained with labels.
 
-           - Analyzing a document with composed models is identical to analyzing a document with a single model, the analyze result returns a ```docType``` property indicating which of the component models was selected for analyzing the document. There's no change in pricing for analyzing a document with an individual custom model or a composed custom model.
+           - Analyzing a document with composed models is identical to analyzing a document with a single model, the Analyze result returns a ```docType``` property indicating which of the component models was selected for analyzing the document. There's no change in pricing for analyzing a document with an individual custom model or a composed custom model.
 
            Learn more about [composed models](concept-custom.md).
 
@@ -431,11 +431,11 @@ sections:
           Consider setting a longer duration period for the time you're using your storage account with Document Intelligence.
 
       - question: |
-           If my storage account is behind a VNet or firewall, how do I give Document Intelligence access to my storage account data?
+           If my storage account is behind a virtual network or firewall, how do I give Document Intelligence access to my storage account data?
         answer: |
-            If you have an Azure storage account protected by a Virtual Network (VNet) or firewall, Document Intelligence can’t directly access your storage account. However, Private Azure storage account access and authentication support [managed identities for Azure resources](../../active-directory/managed-identities-azure-resources/overview.md). Once a managed identity is enabled, the Document Intelligence service can access your storage account using an assigned managed identity credential.
+            If you have an Azure storage account protected by a Virtual Network (virtual network) or firewall, Document Intelligence can’t directly access your storage account. However, Private Azure storage account access and authentication support [managed identities for Azure resources](../../active-directory/managed-identities-azure-resources/overview.md). Once a managed identity is enabled, the Document Intelligence service can access your storage account using an assigned managed identity credential.
 
-            If you intend to analyze your private storage account data with FOTT, the tool must be deployed behind the VNet or firewall.
+            If you intend to analyze your private storage account data with FOTT, the tool must be deployed behind the `virtual network` or firewall.
 
             Learn how to [create and use a managed identity for your Document Intelligence resource](managed-identities.md)
 
@@ -471,7 +471,7 @@ sections:
           Why am I receiving an AuthorizationFailure error on Project Sharing, Auto Label, or OCR Upgrade when my Document Intelligence or Storage Account resource is configured with a firewall?
         answer: |
 
-          Add our website IP address, 20.3.165.95, to the firewall allowlist for both Document Intelligence and Storage Account resources. This is Document Intelligence Studio's dedicated IP address and can be safely allowed.
+          Add our website IP address, 20.3.165.95, to the firewall allowlist for both Document Intelligence and Storage Account resources. This unique address is Document Intelligence Studio's dedicated IP address and can be safely allowed.
 
       - question: |
           Can I reuse or customize the labeling experience from Studio and build it into my own application?

articles/ai-services/document-intelligence/how-to-guides/includes/v3-0/rest-api.md

@@ -7,7 +7,7 @@ ms.service: azure-ai-document-intelligence
 ms.custom:
   - ignite-2023
 ms.topic: include
-ms.date: 08/21/2023
+ms.date: 11/21/2023
 ms.author: lajanuar
 ---
 
@@ -68,7 +68,7 @@ Open a console window and run the following cURL command. The commands include t
 curl -i -X POST "%FR_ENDPOINT%formrecognizer/documentModels/<modelId>:analyze?api-version=2023-07-31" -H "Content-Type: application/json" -H "Ocp-Apim-Subscription-Key: %FR_KEY%" --data-ascii "{'urlSource': '<document-url>'}"
 ```
 
-To enable add-on capabilities, use the `features` query parameter in the POST request. There are four add-on capabilities available with the 2023-07-31 (GA) release: *ocr.highResolution*, *ocr.formula*, *ocr.font*, and *queryFields.premium*. To learn more about each of the capabilities, see [Custom models](../../../concept-accuracy-confidence.md).
+To enable add-on capabilities, use the `features` query parameter in the POST request. There are four add-on capabilities available with the `2023-07-31` (GA) release: *ocr.highResolution*, *ocr.formula*, *ocr.font*, and *queryFields.premium*. To learn more about each of the capabilities, see [Custom models](../../../concept-accuracy-confidence.md).
 
 You can only call the *highResolution*, *formula*, and *font* capabilities for the Read and Layout model, and the *queryFields* capability for the General Documents model. The following example shows how to call the *highResolution*, *formula*, and *font* capabilities for the Layout model.
 
@@ -87,7 +87,7 @@ You receive a `202 (Success)` response that includes an `Operation-location` hea
 
 ### Get analyze result (GET Request)
 
-After you call the [Analyze document](/rest/api/aiservices/document-models/analyze-document?view=rest-aiservices-2023-07-31&preserve-view=true&tabs=HTTP) API, call the [Get analyze result}(/rest/api/aiservices/document-models/get-analyze-result?view=rest-aiservices-2023-07-31&preserve-view=true&tabs=HTTP) API to get the status of the operation and the extracted data.
+After you call the [Analyze document](/rest/api/aiservices/document-models/analyze-document?view=rest-aiservices-2023-07-31&preserve-view=true&tabs=HTTP) API, call the [`Get analyze` result}(/rest/api/aiservices/document-models/get-analyze-result?view=rest-aiservices-2023-07-31&preserve-view=true&tabs=HTTP) API to get the status of the operation and the extracted data.
 
 <!-- markdownlint-disable MD024 -->
 
@@ -127,9 +127,9 @@ The *json_pp* command tool ships with macOS and can be used as a JSON formatter
 
 #### [Linux](#tab/linux)
 
-The *json_pp* command line tool is preinstalled in most Linux distributions. If it's not included, you can use your distribution's package manager to install it.
+The *json_pp* command line tool is preinstalled in most Linux distributions. If it isn't included, you can use your distribution's package manager to install it.
 
-- Pretty print the JSON output by including `| json_pp` with your GET requests.
+- Pretty print the JSON output by including `| json_pp` with your `GET` requests.
 
   ```console
   curl -i -X GET "<endpoint>formrecognizer/documentModels/prebuilt-read/analyzeResults/0e49604a-2d8e-4b15-b6b8-bb456e5d3e0a?api-version=2023-07-31"-H "Ocp-Apim-Subscription-Key: <subscription key>" | json_pp
@@ -159,7 +159,7 @@ curl -i -X GET "<POST response>" -H "Ocp-Apim-Subscription-Key: %FR_KEY%" | `<js
 
 You receive a `200 (Success)` response with JSON output. The first field, `status`, indicates the status of the operation. If the operation isn't complete, the value of `status` is `running` or `notStarted`. Call the API again, either manually or through a script. We recommend an interval of one second or more between calls.
 
-Visit the Azure samples repository on GitHub to view the GET response for each of the Document Intelligence models:
+Visit the Azure samples repository on GitHub to view the `GET` response for each of the Document Intelligence models:
 
 | Model | Output URL |
 | --- | --- |

articles/ai-services/document-intelligence/index.yml

@@ -152,7 +152,7 @@ landingContent:
             url: https://techcommunity.microsoft.com/t5/ai-applied-ai-blog/process-large-scale-pdf-or-images-to-extract-information-forms/ba-p/3481533
           - text: Document Intelligence Solution Accelerator
             url: https://techcommunity.microsoft.com/t5/ai-applied-ai-blog/form-recognizer-solution-accelerator/ba-p/3262782
-          - text: Extracting information from unstructured document (e.g., contracts) with Azure AI Document Intelligence
+          - text: Extracting information from unstructured document (for example, contracts) with Azure AI Document Intelligence
             url:  https://techcommunity.microsoft.com/t5/ai-applied-ai-blog/extracting-information-from-unstructured-document-e-g-contracts/ba-p/3267207
           - text: Improve Customer Loyalty Program Automation with Azure AI Document Intelligence
             url:  https://techcommunity.microsoft.com/t5/ai-applied-ai-blog/improve-customer-loyalty-program-automation-with-azure-form/ba-p/3069233