edit for pub

Author: laujan

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

@@ -8,7 +8,7 @@ ms.service: azure-ai-document-intelligence
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 02/09/2024
+ms.date: 02/21/2024
 ms.author: lajanuar
 ---
 
@@ -184,15 +184,16 @@ Document Intelligence v2.1 supports the following tools, applications, and libra
 The layout model extracts text, selection marks, tables, paragraphs, and paragraph types (`roles`) from your documents.
 
 > [!NOTE]
-> Microsoft Word and HTML file are supported since `2023-10-31-preview`. Compared with PDF and images, below features are not supported:
-> - There are no angle, width/height and unit with each page object.
-> - For each object detected, there is no bounding polygon or bounding region.
-> - Page range (`pages`) is not supported as a parameter.
-> - No `lines` object.
+> Version `2023-10-31-preview` and later support Microsoft Word and HTML files. The following features are not supported:
+>
+> * There are no angle, width/height and unit with each page object.
+> * For each object detected, there is no bounding polygon or bounding region.
+> * Page range (`pages`) is not supported as a parameter.
+> * No `lines` object.
 
 ### Pages
 
-The pages collection is a list of pages within the document. For each page, it is represented with the sequential number of the page within the document, the orientation angle, which could indicate if the page has been rotated, the width and height (dimentions in pixels) of the page. The page units in the model output are computed as shown:
+The pages collection is a list of pages within the document. Each page is represented sequentially within the document and includes the orientation angle indicating if the page is rotated and the width and height (dimensions in pixels). The page units in the model output are computed as shown:
 
  **File format**   | **Computed page unit**   | **Total pages**  |
 | --- | --- | --- |
@@ -271,11 +272,11 @@ The new machine-learning based page object detection extracts logical roles like
 
 ```
 
-### Text, lines and words
+### Text, lines, and words
 
 The document layout model in Document Intelligence extracts print and handwritten style text as `lines` and `words`. The `styles` collection includes any handwritten style for lines if detected along with the spans pointing to the associated text. This feature applies to [supported handwritten languages](language-support.md).
 
-For Microsoft Word, Excel, PowerPoint, and HTML, Document Intelligence version 2023-10-31-preview the Layout model extracts all embedded text as is. Texts are extrated as words and paragraphs. Embedded images are not supported.
+For Microsoft Word, Excel, PowerPoint, and HTML, Document Intelligence version 2023-10-31-preview the Layout model extracts all embedded text as is. Texts are extrated as words and paragraphs. Embedded images aren't supported.
 
 ```json
 "words": [
@@ -312,7 +313,8 @@ The response includes classifying whether each text line is of handwriting style
     ]
 }
 ```
-If you have turned on [font/style addon capability](concept-add-on-capabilities.md#font-property-extraction), you will also get the font/style result as part of the `styles` object.
+
+If you enable the [font/style addon capability](concept-add-on-capabilities.md#font-property-extraction), you also get the font/style result as part of the `styles` object.
 
 ### Selection marks
 

articles/ai-services/document-intelligence/concept-model-overview.md

@@ -8,7 +8,7 @@ ms.service: azure-ai-document-intelligence
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 02/06/2024
+ms.date: 02/21/2024
 ms.author: lajanuar
 ---
 
@@ -38,7 +38,7 @@ ms.author: lajanuar
 ::: moniker-end
 
 ::: moniker range=">=doc-intel-2.1.0"
- Azure AI Document Intelligence supports a wide variety of models that enable you to add intelligent document processing to your apps and flows. You can use a prebuilt domain-specific model or train a custom model tailored to your specific business need and use cases. Document Intelligence can be used with the REST API or Python, C#, Java, and JavaScript SDKs.
+ Azure AI Document Intelligence supports a wide variety of models that enable you to add intelligent document processing to your apps and flows. You can use a prebuilt domain-specific model or train a custom model tailored to your specific business need and use cases. Document Intelligence can be used with the REST API or Python, C#, Java, and JavaScript client libraries.
 ::: moniker-end
 
 ## Model overview
@@ -88,7 +88,7 @@ Add-On* - Query fields are priced differently than the other add-on features. Se
 | [Read OCR](#read-ocr) | Extract print and handwritten text including words, locations, and detected languages.|
 | [Layout analysis](#layout-analysis)  | Extract text and document layout elements like tables, selection marks, titles, section headings, and more.|
 |**Prebuilt models**||
-| [Health insurance card](#health-insurance-card) | Automate healthcare processes by extracting insurer, member, prescription, group number and other key information from US health insurance cards.|
+| [Health insurance card](#health-insurance-card) | Automate healthcare processes by extracting insurer, member, prescription, group number, and other key information from US health insurance cards.|
 | [US Tax document models](#us-tax-documents) | Process US tax forms to extract employee, employer, wage, and other information.  |
 | [Contract](#contract) | Extract agreement and party details.|
 | [Invoice](#invoice)  | Automate invoices. |
@@ -97,8 +97,8 @@ Add-On* - Query fields are priced differently than the other add-on features. Se
 | [Business card](#business-card)  | Scan business cards to extract key fields and data into your applications. |
 |**Custom models**||
 | [Custom model (overview)](#custom-models) |  Extract data from forms and documents specific to your business. Custom models are trained for your distinct data and use cases. |
-| [Custom extraction models](#custom-extraction)| &#9679; **Custom template models** use layout cues to extract values from documents and are suitable to extract fields from highly structured documents with defined visual templates.</br>&#9679; **Custom neural models** are  trained on various document types to extract fields from structured, semi-structured and unstructured documents.|
-| [Custom classification model](#custom-classifier)| The **Custom classification model** can classify each page in an input file to identify the document(s) within and can also identify multiple documents or multiple instances of a single document within an input file.
+| [Custom extraction models](#custom-extraction)| &#9679; **Custom template models** use layout cues to extract values from documents and are suitable to extract fields from highly structured documents with defined visual templates.</br>&#9679; **Custom neural models** are  trained on various document types to extract fields from structured, semi-structured, and unstructured documents.|
+| [Custom classification model](#custom-classifier)| The **Custom classification model** can classify each page in an input file to identify the documents within and can also identify multiple documents or multiple instances of a single document within an input file.
 | [Composed models](#composed-models) | Combine several custom models into a single model to automate processing of diverse document types with a single composed model.
 
 For all models, except Business card model, Document Intelligence now supports add-on capabilities to allow for more sophisticated analysis. These optional capabilities can be enabled and disabled depending on the scenario of the document extraction. There are seven add-on capabilities available for the `2023-07-31` (GA) and later API version:
@@ -109,7 +109,7 @@ For all models, except Business card model, Document Intelligence now supports a
 * [`barcodes`](concept-add-on-capabilities.md#barcode-property-extraction)
 * [`languages`](concept-add-on-capabilities.md#language-detection)
 * [`keyValuePairs`](concept-add-on-capabilities.md#key-value-pairs) (2023-10-31-preview)
-* [`queryFields`](concept-add-on-capabilities.md#query-fields) (2023-10-31-preview). `Not available with the US.Tax models`
+* [`queryFields`](concept-add-on-capabilities.md#query-fields) (2023-10-31-preview) `Not available with the US.Tax models`
 
 ## Analysis features
 
@@ -167,7 +167,7 @@ The US tax document models analyze and extract key fields and line items from a
   |US Tax 1098|Extract mortgage interest details.|**prebuilt-tax.us.1098**|
   |US Tax 1098-E|Extract student loan interest details.|**prebuilt-tax.us.1098E**|
   |US Tax 1098-T|Extract qualified tuition details.|**prebuilt-tax.us.1098T**|
-  |US Tax 1099|Extract Information from 1099 forms.|**prebuilt-tax.us.1099(variations)**|
+  |US Tax 1099|Extract wage information details.|**prebuilt-tax.us.1099(variations)**|
 
 ***Sample W-2 document processed using [Document Intelligence Studio](https://formrecognizer.appliedai.azure.com/studio/prebuilt?formType=tax.us.w2)***:
 
@@ -193,7 +193,7 @@ The US tax document models analyze and extract key fields and line items from a
 
 :::image type="icon" source="media/studio/invoice.png":::
 
-The invoice model automates processing of invoices to extracts customer name, billing address, due date, and amount due, line items and other key data. Currently, the model supports English, Spanish, German, French, Italian, Portuguese, and Dutch invoices.
+The invoice model automates processing of invoices to extracts customer name, billing address, due date, and amount due, line items, and other key data. Currently, the model supports English, Spanish, German, French, Italian, Portuguese, and Dutch invoices.
 
 ***Sample invoice processed using [Document Intelligence Studio](https://formrecognizer.appliedai.azure.com/studio/prebuilt?formType=invoice)***:
 
@@ -263,7 +263,7 @@ Custom extraction model can be one of two types, **custom template** or **custom
 
 :::image type="icon" source="media/studio/custom-classifier.png":::
 
-The custom classification model enables you to identify the document type prior to invoking the extraction model.  The classification model is available starting with the `2023-07-31 (GA)` API. Training a custom classification model requires at least two distinct classes and a minimum of five samples per class.
+The custom classification model enables you to identify the document type before invoking the extraction model. The classification model is available starting with the `2023-07-31 (GA)` API. Training a custom classification model requires at least two distinct classes and a minimum of five samples per class.
 
 > [!div class="nextstepaction"]
 > [Learn more: custom classification model](concept-custom-classifier.md)
@@ -399,7 +399,7 @@ The business card model analyzes and extracts key information from business card
 
 #### Composed custom model
 
-A composed model is created by taking a collection of custom models and assigning them to a single model built from your form types. You can assign multiple custom models to a composed model called with a single model ID. you can assign up to 100 trained custom models to a single composed model.
+A composed model is created by taking a collection of custom models and assigning them to a single model built from your form types. You can assign multiple custom models to a composed model called with a single model ID. You can assign up to 100 trained custom models to a single composed model.
 
 ***Composed model dialog window using the [Sample Labeling tool](https://formrecognizer.appliedai.azure.com/studio/customform/projects)***:
 
@@ -436,15 +436,15 @@ A composed model is created by taking a collection of custom models and assignin
 
 ::: moniker range=">=doc-intel-3.0.0"
 
-* Try processing your own forms and documents with the [Document Intelligence Studio](https://formrecognizer.appliedai.azure.com/studio)
+* Try processing your own forms and documents with the [Document Intelligence Studio](https://formrecognizer.appliedai.azure.com/studio).
 
 * Complete a [Document Intelligence quickstart](quickstarts/get-started-sdks-rest-api.md?view=doc-intel-3.0.0&preserve-view=true) and get started creating a document processing app in the development language of your choice.
 
 ::: moniker-end
 
 ::: moniker range="doc-intel-2.1.0"
 
-* Try processing your own forms and documents with the [Document Intelligence Sample Labeling tool](https://fott-2-1.azurewebsites.net/)
+* Try processing your own forms and documents with the [Document Intelligence Sample Labeling tool](https://fott-2-1.azurewebsites.net/).
 
 * Complete a [Document Intelligence quickstart](quickstarts/get-started-sdks-rest-api.md?view=doc-intel-2.1.0&preserve-view=true) and get started creating a document processing app in the development language of your choice.
 

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

@@ -109,23 +109,24 @@ See our [Language Support—document analysis models](language-support-ocr.md) p
 
 > [!NOTE]
 > Microsoft Word and HTML file are supported in v3.1 and later versions. Compared with PDF and images, below features are not supported:
-> - There are no angle, width/height and unit with each page object.
-> - For each object detected, there is no bounding polygon or bounding region.
-> - Page range (`pages`) is not supported as a parameter.
-> - No `lines` object.
+>
+> * There are no angle, width/height and unit with each page object.
+> * For each object detected, there is no bounding polygon or bounding region.
+> * Page range (`pages`) is not supported as a parameter.
+> * No `lines` object.
 
 ### Pages
 
-The pages collection is a list of pages within the document. For each page, it is represented with the sequential number of the page within the document, the orientation angle, which could indicate if the page has been rotated, the width and height (dimentions in pixels) of the page. The page units in the model output are computed as shown:
+The pages collection is a list of pages within the document. Each page is represented sequentially within the document and includes the orientation angle indicating if the page is rotated and the width and height (dimensions in pixels). The page units in the model output are computed as shown:
 
- **File format**   | **Computed page unit**   | **Total pages**  |
+|**File format**   | **Computed page unit**   | **Total pages**  |
 | --- | --- | --- |
 |Images (JPEG/JPG, PNG, BMP, HEIF) | Each image = 1 page unit | Total images  |
 |PDF | Each page in the PDF = 1 page unit | Total pages in the PDF |
 |TIFF | Each image in the TIFF = 1 page unit | Total images in the PDF |
 |Word (DOCX)  | Up to 3,000 characters = 1 page unit, embedded or linked images not supported | Total pages of up to 3,000 characters each |
-|Excel (XLSX)  | Each worksheet = 1 page unit, embedded or linked images not supported | Total worksheets | 
-|PowerPoint (PPTX) |  Each slide = 1 page unit, embedded or linked images not supported | Total slides | 
+|Excel (XLSX)  | Each worksheet = 1 page unit, embedded or linked images not supported | Total worksheets |
+|PowerPoint (PPTX) |  Each slide = 1 page unit, embedded or linked images not supported | Total slides |
 |HTML | Up to 3,000 characters = 1 page unit, embedded or linked images not supported | Total pages of up to 3,000 characters each |
 
 ```json
@@ -165,7 +166,7 @@ The Read OCR model in Document Intelligence extracts all identified blocks of te
 
 The Read OCR model extracts print and handwritten style text as `lines` and `words`. The model outputs bounding `polygon` coordinates and `confidence` for the extracted words. The `styles` collection includes any handwritten style for lines if detected along with the spans pointing to the associated text. This feature applies to [supported handwritten languages](language-support.md).
 
-For Microsoft Word, Excel, PowerPoint, and HTML, Document Intelligence Read model v3.1 and later versions extracts all embedded text as is. Texts are extrated as words and paragraphs. Embedded images are not supported.
+For Microsoft Word, Excel, PowerPoint, and HTML, Document Intelligence Read model v3.1 and later versions extracts all embedded text as is. Texts are extrated as words and paragraphs. Embedded images aren't supported.
 
 
 ```json
@@ -204,7 +205,7 @@ The response includes classifying whether each text line is of handwriting style
 }
 ```
 
-If you have turned on [font/style addon capability](concept-add-on-capabilities.md#font-property-extraction), you will also get the font/style result as part of the `styles` object.
+If you enabled the [font/style addon capability](concept-add-on-capabilities.md#font-property-extraction), you also get the font/style result as part of the `styles` object.
 
 ## Next steps
 

articles/ai-services/document-intelligence/overview.md

@@ -8,7 +8,7 @@ ms.service: azure-ai-document-intelligence
 ms.custom:
   - ignite-2023
 ms.topic: overview
-ms.date: 02/06/2024
+ms.date: 02/21/2024
 ms.author: lajanuar
 monikerRange: '<=doc-intel-4.0.0'
 ---
@@ -217,14 +217,14 @@ Prebuilt models enable you to add intelligent document processing to your apps a
 :::row:::
     :::column:::
         * **Classification model**</br>
-         ✔️ Custom classifiers identify document types prior to invoking an extraction model.
+         ✔️ Custom classifiers identify document types before invoking an extraction model.
     :::column-end:::
 :::row-end:::
 
 :::row:::
    :::column span="":::
       :::image type="icon" source="media/overview/icon-custom-classifier.png" link="#custom-classification-model":::</br>
-    [**Custom classifier**](#custom-classification-model) | Identify designated document types (classes) </br>prior to invoking an extraction model.
+    [**Custom classifier**](#custom-classification-model) | Identify designated document types (classes) </br>before invoking an extraction model.
    :::column-end:::
 :::row-end:::
 
@@ -266,7 +266,7 @@ Document Intelligence supports optional features that can be enabled and disable
 
 ✓ - Enabled</br>
 O - Optional</br>
-\* - Premium features incur extra costs
+\* - Premium features incur extra costs.
 
 ## Models and development options
 
@@ -416,7 +416,7 @@ You can use Document Intelligence to automate document processing in application
 
 | Model ID |Description|Development options |
 |----------|--------------|-----------------|
-|**prebuilt-tax.us.1099(Variations)**|Extract information from 1099 form variations.|&#9679; [**Document Intelligence Studio**](https://formrecognizer.appliedai.azure.com/studio)</br>&#9679; [**REST API**](https://westus.dev.cognitive.microsoft.com/docs/services?pattern=intelligence)|
+|**prebuilt-tax.us.1099(Variations)**|Extract information from 1099-form variations.|&#9679; [**Document Intelligence Studio**](https://formrecognizer.appliedai.azure.com/studio)</br>&#9679; [**REST API**](https://westus.dev.cognitive.microsoft.com/docs/services?pattern=intelligence)|
 
 > [!div class="nextstepaction"]
 > [Return to model types](#prebuilt-models)
@@ -550,17 +550,17 @@ Use the links in the table to learn more about each model and browse the API ref
 
 ::: moniker range=">=doc-intel-3.0.0"
 
-* [Choose a Document Intelligence model](choose-model-feature.md)
+* [Choose a Document Intelligence model](choose-model-feature.md).
 
-* Try processing your own forms and documents with the [Document Intelligence Studio](https://formrecognizer.appliedai.azure.com/studio)
+* Try processing your own forms and documents with the [Document Intelligence Studio](https://formrecognizer.appliedai.azure.com/studio).
 
 * Complete a [Document Intelligence quickstart](quickstarts/get-started-sdks-rest-api.md?view=doc-intel-3.0.0&preserve-view=true) and get started creating a document processing app in the development language of your choice.
 
 ::: moniker-end
 
 ::: moniker range="doc-intel-2.1.0"
 
-* Try processing your own forms and documents with the [Document Intelligence Sample Labeling tool](https://fott-2-1.azurewebsites.net/)
+* Try processing your own forms and documents with the [Document Intelligence Sample Labeling tool](https://fott-2-1.azurewebsites.net/).
 
 * Complete a [Document Intelligence quickstart](quickstarts/get-started-sdks-rest-api.md?view=doc-intel-2.1.0&preserve-view=true) and get started creating a document processing app in the development language of your choice.