What is the Document Intelligence layout model?

This content applies to: v4.0 (GA) | Previous versions: v3.1 (GA) v3.0 (GA) v2.1 (GA)

The Azure AI Document Intelligence layout model is an advanced document-analysis API based on machine learning. The model is available in the Document Intelligence cloud. You can use it to take documents in various formats and return structured data representations of the documents. The model combines an enhanced version of the powerful optical character recognition (OCR) capabilities with deep learning models to extract text, tables, selection marks, and document structure.

Document structure layout analysis

Document structure layout analysis is the process of analyzing a document to extract regions of interest and their interrelationships. The goal is to extract text and structural elements from the page to build better semantic understanding models. There are two types of roles in a document layout:

• Geometric roles: Text, tables, figures, and selection marks are examples of geometric roles.
• Logical roles: Titles, headings, and footers are examples of logical roles of texts.

The following illustration shows the typical components in an image of a sample page.

Development options

Document Intelligence v4.0: 2024-11-30 (GA) supports the following tools, applications, and libraries.

Supported languages

For a complete list of supported languages, see Language support: Document analysis models.

Supported file types

Document Intelligence v4.0: 2024-11-30 (GA) layout model supports the following file formats:

Input requirements

• Photos and scans: For best results, provide one clear photo or high-quality scan per document.
• PDFs and TIFFs: For PDFs and TIFFs, up to 2,000 pages can be processed. (With a free-tier subscription, only the first two pages are processed.)
• Password locks: If your PDFs are password-locked, you must remove the lock before submission.
• File size: The file size for analyzing documents is 500 MB for the paid (S0) tier and 4 MB for the free (F0) tier.
• Image dimensions: The image dimensions must be between 50 pixels x 50 pixels and 10,000 pixels x 10,000 pixels.
• Text height: The minimum height of the text to be extracted is 12 pixels for a 1024 x 768 pixel image. This dimension corresponds to about 8-point text at 150 dots per inch.
• Custom model training: The maximum number of pages for training data is 500 for the custom template model and 50,000 for the custom neural model.
• Custom extraction model training: The total size of training data is 50 MB for the template model and 1 GB for the neural model.
• Custom classification model training: The total size of training data is 1 GB with a maximum of 10,000 pages. For 2024-11-30 (GA), the total size of training data is 2 GB with a maximum of 10,000 pages.
• Office file types (DOCX, XLSX, PPTX): The maximum string length limit is 8 million characters.

For more information on model usage, quotas, and service limits, see Service limits.

Get started with the layout model

See how data, including text, tables, table headers, selection marks, and structure information, is extracted from documents by using Document Intelligence. You need the following resources:

• An Azure subscription. You can create one for free.

• A Document Intelligence instance in the Azure portal. You can use the free pricing tier (F0) to try the service. After your resource deploys, select Go to resource to get your key and endpoint.

  

After you retrieve your key and endpoint, use the following development options to build and deploy your Document Intelligence applications.

Data extraction

The layout model extracts structural elements from your documents. The following structural elements are described in the remainder of this article along with guidance on how to extract them from your document input:

• Pages
• Paragraphs
• Text, lines, and words
• Selection marks
• Tables
• Output response to markdown
• Figures
• Sections

Run the sample layout document analysis within Document Intelligence Studio. Then go to the results tab and access the full JSON output.

Pages

The pages collection is a list of pages within the document. Each page is represented sequentially within the document and includes the orientation angle, which indicates if the page is rotated, and the width and height (dimensions in pixels). The page units in the model output are computed as shown in the following table.

# Analyze pages.
for page in result.pages:
print(f"----Analyzing layout from page #{page.page_number}----")
print(f"Page has width: {page.width} and height: {page.height}, measured with unit: {page.unit}")

"pages": [
    {
        "pageNumber": 1,
        "angle": 0,
        "width": 915,
        "height": 1190,
        "unit": "pixel",
        "words": [],
        "lines": [],
        "spans": []
    }
]

Extract selected pages

For large multipage documents, use the pages query parameter to indicate specific page numbers or page ranges for text extraction.

Paragraphs

The layout model extracts all identified blocks of text in the paragraphs collection as a top-level object under analyzeResults. Each entry in this collection represents a text block and includes the extracted text as content and the bounding polygon coordinates. The spans information points to the text fragment within the top-level content property that contains the full text from the document.

"paragraphs": [
    {
        "spans": [],
        "boundingRegions": [],
        "content": "While healthcare is still in the early stages of its Al journey, we are seeing pharmaceutical and other life sciences organizations making major investments in Al and related technologies.\" TOM LAWRY | National Director for Al, Health and Life Sciences | Microsoft"
    }
]

Paragraph roles

The new page object detection based on machine learning extracts logical roles like titles, section headings, page headers, page footers, and more. The Document Intelligence layout model assigns certain text blocks in the paragraphs collection with their specialized role or type predicted by the model.

It's best to use paragraph roles with unstructured documents to help understand the layout of the extracted content for a richer semantic analysis. The following paragraph roles are supported.

{
    "paragraphs": [
                {
                    "spans": [],
                    "boundingRegions": [],
                    "role": "title",
                    "content": "NEWS TODAY"
                },
                {
                    "spans": [],
                    "boundingRegions": [],
                    "role": "sectionHeading",
                    "content": "Mirjam Nilsson"
                }
    ]
}

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 that point to the associated text. This feature applies to supported handwritten languages.

For Microsoft Word, Excel, PowerPoint, and HTML, the Document Intelligence v4.0 2024-11-30 (GA) layout model extracts all embedded text as is. Texts are extracted as words and paragraphs. Embedded images aren't supported.

# Analyze lines.
if page.lines:
    for line_idx, line in enumerate(page.lines):
    words = get_words(page, line)
    print(
        f"...Line # {line_idx} has word count {len(words)} and text '{line.content}' "
        f"within bounding polygon '{line.polygon}'"
    )

    # Analyze words.
    for word in words:
        print(f"......Word '{word.content}' has a confidence of {word.confidence}")

"words": [
    {
        "content": "While",
        "polygon": [],
        "confidence": 0.997,
        "span": {}
    },
],
"lines": [
    {
        "content": "While healthcare is still in the early stages of its Al journey, we",
        "polygon": [],
        "spans": [],
    }
]

Handwritten style for text lines

The response includes whether each text line is in a handwritten style or not, along with a confidence score. For more information, see Handwritten language support. The following example shows an example JSON snippet.

"styles": [
{
    "confidence": 0.95,
    "spans": [
    {
        "offset": 509,
        "length": 24
    }
    "isHandwritten": true
    ]
}

If you enable the font/style add-on capability, you also get the font/style result as part of the styles object.

Selection marks

The layout model also extracts selection marks from documents. Extracted selection marks appear within the pages collection for each page. They include the bounding polygon, confidence, and selection state (selected/unselected). The text representation (that is, :selected: and :unselected) is also included as the starting index (offset) and length that references the top-level content property that contains the full text from the document.

# Analyze selection marks.
if page.selection_marks:
    for selection_mark in page.selection_marks:
        print(
            f"Selection mark is '{selection_mark.state}' within bounding polygon "
            f"'{selection_mark.polygon}' and has a confidence of {selection_mark.confidence}"
        )

{
    "selectionMarks": [
        {
            "state": "unselected",
            "polygon": [],
            "confidence": 0.995,
            "span": {
                "offset": 1421,
                "length": 12
            }
        }
    ]
}

Tables

Extracting tables is a key requirement for processing documents that contain large volumes of data typically formatted as tables. The layout model extracts tables in the pageResults section of the JSON output. Extracted table information includes the number of columns and rows, row span, and column span.

Each cell with its bounding polygon is output along with information whether the area is recognized as columnHeader or not. The model supports extracting tables that are rotated. Each table cell contains the row and column index and bounding polygon coordinates. For the cell text, the model outputs the span information that contains the starting index (offset). The model also outputs the length within the top-level content that contains the full text from the document.

Here are a few factors to consider when you use the Document Intelligence bale extraction capability:

• Is the data that you want to extract presented as a table, and is the table structure meaningful?
• Can the data fit in a two-dimensional grid if the data isn't in a table format?
• Do your tables span multiple pages? If so, to avoid having to label all the pages, split the PDF into pages before you send it to Document Intelligence. After the analysis, post-process the pages to a single table.
• See Tabular fields if you create custom models. Dynamic tables have a variable number of rows for each column. Fixed tables have a constant number of rows for each column.

if result.tables:
    for table_idx, table in enumerate(result.tables):
        print(f"Table # {table_idx} has {table.row_count} rows and " f"{table.column_count} columns")
        if table.bounding_regions:
            for region in table.bounding_regions:
                print(f"Table # {table_idx} location on page: {region.page_number} is {region.polygon}")
        # Analyze cells.
        for cell in table.cells:
            print(f"...Cell[{cell.row_index}][{cell.column_index}] has text '{cell.content}'")
            if cell.bounding_regions:
                for region in cell.bounding_regions:
                print(f"...content on page {region.page_number} is within bounding polygon '{region.polygon}'")

{
    "tables": [
        {
            "rowCount": 9,
            "columnCount": 4,
            "cells": [
                {
                    "kind": "columnHeader",
                    "rowIndex": 0,
                    "columnIndex": 0,
                    "columnSpan": 4,
                    "content": "(In millions, except earnings per share)",
                    "boundingRegions": [],
                    "spans": []
                    },
            ]
        }
    ]
}

Output response to Markdown format

The layout API can output the extracted text in Markdown format. Use the outputContentFormat=markdown to specify the output format in Markdown. The Markdown content is output as part of the content section.

document_intelligence_client = DocumentIntelligenceClient(endpoint=endpoint, credential=AzureKeyCredential(key))
poller = document_intelligence_client.begin_analyze_document(
    "prebuilt-layout",
    AnalyzeDocumentRequest(url_source=url),
    output_content_format=ContentFormat.MARKDOWN,
)

PageHeader="This is the header of the document."

This is title
===
# 1\. Text
Latin refers to an ancient Italic language originating in the region of Latium in ancient Rome.
# 2\. Page Objects
## 2.1 Table
Here's a sample table below, designed to be simple for easy understand and quick reference.
| Name | Corp | Remark |
| - | - | - |
| Foo | | |
| Bar | Microsoft | Dummy |
Table 1: This is a dummy table
## 2.2. Figure
<figure>
<figcaption>

Figure 1: Here is a figure with text
</figcaption>

![](figures/0)
FigureContent="500 450 400 400 350 250 200 200 200- Feb"
</figure>

# 3\. Others
Al Document Intelligence is an Al service that applies advanced machine learning to extract text, key-value pairs, tables, and structures from documents automatically and accurately:
    :selected:
clear
    :selected:
precise
    :unselected:
vague
    :selected:
coherent
    :unselected:
Incomprehensible
Turn documents into usable data and shift your focus to acting on information rather than compiling it. Start with prebuilt models or create custom models tailored to your documents both on premises and in the cloud with the Al Document Intelligence studio or SDK.
Learn how to accelerate your business processes by automating text extraction with Al Document Intelligence. This webinar features hands-on demos for key use cases such as document processing, knowledge mining, and industry-specific Al model customization.
PageFooter="This is the footer of the document."
PageFooter="1 | Page"

Figures

Figures (charts and images) in documents play a crucial role in complementing and enhancing the textual content. They provide visual representations that aid in the understanding of complex information. The figures object detected by the layout model has key properties like:

• boundingRegions: The spatial locations of the figure on the document pages, including the page number and the polygon coordinates that outline the figure's boundary.
• spans: The text spans related to the figure that specify their offsets and lengths within the document's text. This connection helps in associating the figure with its relevant textual context.
• elements: The identifiers for text elements or paragraphs within the document that are related to or describe the figure.
• caption: The description if there is one.

When output=figures is specified during the initial analyze operation, the service generates cropped images for all detected figures that can be accessed via /analyeResults/{resultId}/figures/{figureId}. The FigureId value is the ID included in each figure object, following an undocumented convention of {pageNumber}.{figureIndex} where figureIndex resets to one per page.

For v4.0 2024-11-30 (GA), the bounding regions for figures and tables cover only the core content and exclude the associated caption and footnotes.

# Analyze figures.
if result.figures:
    for figures_idx,figures in enumerate(result.figures):
        print(f"Figure # {figures_idx} has the following spans:{figures.spans}")
        for region in figures.bounding_regions:
            print(f"Figure # {figures_idx} location on page:{region.page_number} is within bounding polygon '{region.polygon}'")

{
    "figures": [
        {
        "id": "{figureId}",
        "boundingRegions": [],
        "spans": [],
        "elements": [
            "/paragraphs/15",
            ...
        ],
        "caption": {
            "content": "Here is a figure with some text",
            "boundingRegions": [],
            "spans": [],
            "elements": [
            "/paragraphs/15"
            ]
        }
        }
    ]
}

Sections

Hierarchical document structure analysis is pivotal in organizing, comprehending, and processing extensive documents. This approach is vital for semantically segmenting long documents to boost comprehension, facilitate navigation, and improve information retrieval. The advent of retrieval-augmented generation (RAG) in document-generative AI underscores the significance of hierarchical document structure analysis.

The layout model supports sections and subsections in the output, which identifies the relationship of sections and objects within each section. The hierarchical structure is maintained in elements for each section. You can use the output response to Markdown format to easily get the sections and subsections in Markdown.

document_intelligence_client = DocumentIntelligenceClient(endpoint=endpoint, credential=AzureKeyCredential(key))
poller = document_intelligence_client.begin_analyze_document(
    "prebuilt-layout",
    AnalyzeDocumentRequest(url_source=url),
    output_content_format=ContentFormat.MARKDOWN,
)

{
    "sections": [
        {
        "spans": [],
        "elements": [
            "/paragraphs/0",
            "/sections/1",
            "/sections/2",
            "/sections/5"
        ]
        },
...
}

This content applies to: v3.1 (GA) | Latest version: v4.0 (GA) | Previous versions: v3.0 v2.1

This content applies to: v3.0 (GA) | Latest versions: v4.0 (GA) v3.1 | Previous version: v2.1

This content applies to: v2.1 | Latest version: v4.0 (GA)

The Document Intelligence layout model is an advanced document-analysis API. The model is based on machine learning and is available in the Document Intelligence cloud. You can use it to take documents in various formats and return structured data representations of the documents. It combines an enhanced version of the powerful OCR capabilities with deep learning models. You can use it to extract text, tables, selection marks, and document structure.

Document layout analysis

Document structure layout analysis is the process of analyzing a document to extract regions of interest and their interrelationships. The goal is to extract text and structural elements from the page to build better semantic understanding models. There are two types of roles in a document layout:

• Geometric roles: Text, tables, figures, and selection marks are examples of geometric roles.
• Logical roles: Titles, headings, and footers are examples of logical roles of texts.

The following illustration shows the typical components in an image of a sample page.

Supported languages and locales

For a complete list of supported languages, see Language support: Document analysis models.

Document Intelligence v2.1 supports the following tools, applications, and libraries.

Input guidance

Supported file formats:

• Photos and scans: For best results, provide one clear photo or high-quality scan per document.
• PDFs and TIFFs: For PDFs and TIFFs, up to 2,000 pages can be processed with a free-tier subscription. Only the first two pages are processed.
• File size: The file size for analyzing documents is 500 MB for the paid (S0) tier and 4 MB for the free (F0) tier.
• Image dimensions: The image dimensions must be between 50 pixels x 50 pixels and 10,000 pixels x 10,000 pixels.
• Password locks: If your PDFs are password-locked, you must remove the lock before submission.
• Text height: The minimum height of the text to be extracted is 12 pixels for a 1024 x 768 pixel image. This dimension corresponds to about 8-point text at 150 dots per inch.
• Custom model training: The maximum number of pages for training data is 500 for the custom template model and 50,000 for the custom neural model.
• Custom extraction model training: The total size of training data is 50 MB for the template model and 1 GB for the neural model.
• Custom classification model training: The total size of training data is 1 GB with a maximum of 10,000 pages. For 2024-11-30 (GA), the total size of training data is 2 GB with a maximum of 10,000 pages.
• Office file types (DOCX, XLSX, PPTX): The maximum string length limit is 8 million characters.

Input guide

• Supported file formats: JPEG, PNG, PDF, and TIFF.
• Supported number of pages: For PDF and TIFF, up to 2,000 pages are processed. For free tier subscribers, only the first two pages are processed.
• Supported file size: The file size must be less than 50 MB, and the dimensions must be at least 50 x 50 pixels and at most 10,000 x 10,000 pixels.

Get started

You can use Document Intelligence to extract data such as text, tables, table headers, selection marks, and structure information from documents. You need the following resources:

• An Azure subscription. You can create one for free.
• A Document Intelligence instance in the Azure portal. You can use the free pricing tier (F0) to try the service. After your resource deploys, select Go to resource to get your key and endpoint.

After you retrieve your key and endpoint, you can use the following development options to build and deploy your Document Intelligence applications.

REST API

• Document Intelligence v2.1 (Form Recognizer)

Document Intelligence Sample Labeling tool

1. Go to the Document Intelligence Sample Labeling tool.

2. On the sample tool home page, select Use Layout to get text, tables and selection marks.

   

3. In the Document Intelligence service endpoint field, paste the endpoint that you obtained with your Document Intelligence subscription.

4. In the key field, paste the key that you obtained from your Document Intelligence resource.

5. In the Source field, select URL from the dropdown menu. You can use the sample document:

   • Sample document.

   • Select Fetch.

6. Select Run Layout. The Document Intelligence Sample Labeling tool calls the Analyze Layout API to analyze the document.

   

7. View the results. See the highlighted extracted text, detected selection marks, and detected tables.

   

Document Intelligence v2.1 supports the following tools, applications, and libraries.

Extract data

The layout model extracts structural elements from your documents. The structural elements are described here, and the following guidance shows you how to extract them from your document input.

• Page
• Paragraph
• Text, line, and word
• Selection mark
• Table
• Annotations

Extract data

The layout model extracts structural elements from your documents. The structural elements are described here, and the following guidance shows you how to extract them from your document input.

• Page
• Paragraph
• Text, line, and word
• Selection mark
• Table
• Natural reading order
• Select page number or range

Page

The pages collection is a list of pages within the document. Each page is represented sequentially within the document and includes the orientation angle that indicates if the page is rotated and the width and height (dimensions in pixels). The page units in the model output are computed as shown in the following table.

"pages": [
    {
        "pageNumber": 1,
        "angle": 0,
        "width": 915,
        "height": 1190,
        "unit": "pixel",
        "words": [],
        "lines": [],
        "spans": []
    }
]

Extract selected pages from documents

For large multipage documents, use the pages query parameter to indicate specific page numbers or page ranges for text extraction.

Paragraph

The layout model extracts all identified blocks of text in the paragraphs collection as a top-level object under analyzeResults. Each entry in this collection represents a text block and includes the extracted text as contentand the bounding polygon coordinates. The span information points to the text fragment within the top-level content property that contains the full text from the document.

"paragraphs": [
    {
        "spans": [],
        "boundingRegions": [],
        "content": "While healthcare is still in the early stages of its Al journey, we are seeing pharmaceutical and other life sciences organizations making major investments in Al and related technologies.\" TOM LAWRY | National Director for Al, Health and Life Sciences | Microsoft"
    }
]

Paragraph role

The new page object detection based on machine learning extracts logical roles like titles, section headings, page headers, page footers, and more. The Document Intelligence layout model assigns certain text blocks in the paragraphs collection with their specialized role or type predicted by the model. It's best to use paragraph roles with unstructured documents to help understand the layout of the extracted content for a richer semantic analysis. The following paragraph roles are supported.

{
    "paragraphs": [
                {
                    "spans": [],
                    "boundingRegions": [],
                    "role": "title",
                    "content": "NEWS TODAY"
                },
                {
                    "spans": [],
                    "boundingRegions": [],
                    "role": "sectionHeading",
                    "content": "Mirjam Nilsson"
                }
    ]
}

Text, line, and word

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 that point to the associated text. This feature applies to supported handwritten languages.

For Word, Excel, PowerPoint, and HTML, the Document Intelligence v4.0 2024-11-30 (GA) layout model extracts all embedded text as is. Texts are extracted as words and paragraphs. Embedded images aren't supported.

"words": [
    {
        "content": "While",
        "polygon": [],
        "confidence": 0.997,
        "span": {}
    },
],
"lines": [
    {
        "content": "While healthcare is still in the early stages of its Al journey, we",
        "polygon": [],
        "spans": [],
    }
]

Handwritten style

The response includes classifying whether each text line is of handwriting style or not, along with a confidence score. For more information, see Handwritten language support. The following example shows an example JSON snippet.

"styles": [
{
    "confidence": 0.95,
    "spans": [
    {
        "offset": 509,
        "length": 24
    }
    "isHandwritten": true
    ]
}

If you enable the font/style add-on capability, you also get the font/style result as part of the styles object.

Selection mark

The layout model also extracts selection marks from documents. Extracted selection marks appear within the pages collection for each page. They include the bounding polygon, confidence, and selection state (selected/unselected). The text representation (that is, :selected: and :unselected) is also included as the starting index (offset) and length that references the top-level content property that contains the full text from the document.

{
    "selectionMarks": [
        {
            "state": "unselected",
            "polygon": [],
            "confidence": 0.995,
            "span": {
                "offset": 1421,
                "length": 12
            }
        }
    ]
}

Table

Extracting tables is a key requirement for processing documents that contain large volumes of data typically formatted as tables. The layout model extracts tables in the pageResults section of the JSON output. Extracted table information includes the number of columns and rows, row span, and column span. Each cell with its bounding polygon is output along with information whether the area is recognized as columnHeader or not.

The model supports extracting tables that are rotated. Each table cell contains the row and column index and bounding polygon coordinates. For the cell text, the model outputs the span information that contains the starting index (offset). The model also outputs the length within the top-level content that contains the full text from the document.

Here are a few factors to consider when you use the Document Intelligence bale extraction capability:

• Is the data that you want to extract presented as a table, and is the table structure meaningful?
• Can the data fit in a two-dimensional grid if the data isn't in a table format?
• Do your tables span multiple pages? If so, to avoid having to label all the pages, split the PDF into pages before you send it to Document Intelligence. After the analysis, post-process the pages to a single table.
• See Tabular fields if you create custom models. Dynamic tables have a variable number of rows for each column. Fixed tables have a constant number of rows for each column.

{
    "tables": [
        {
            "rowCount": 9,
            "columnCount": 4,
            "cells": [
                {
                    "kind": "columnHeader",
                    "rowIndex": 0,
                    "columnIndex": 0,
                    "columnSpan": 4,
                    "content": "(In millions, except earnings per share)",
                    "boundingRegions": [],
                    "spans": []
                    },
            ]
        }
    ]
}

Annotations

The layout model extracts annotations in documents, such as checks and crosses. The response includes the kind of annotation, along with a confidence score and bounding polygon.

    {
    "pages": [
    {
        "annotations": [
        {
            "kind": "cross",
            "polygon": [...],
            "confidence": 1
        }
        ]
    }
    ]
}

Natural reading order output (Latin only)

You can specify the order in which the text lines are output with the readingOrder query parameter. Use natural for a more human-friendly reading order output, as shown in the following example. This feature is supported only for Latin languages.

Select page number or range for text extraction

For large multipage documents, use the pages query parameter to indicate specific page numbers or page ranges for text extraction. The following example shows a document with 10 pages, with text extracted for both cases, all pages (1-10), and selected pages (3-6).

The Get Analyze Layout Result operation

The second step is to call the Get Analyze Layout Result operation. This operation takes as input the Result ID that the Analyze Layout operation created. It returns a JSON response that contains a status field with the following possible values.

Call this operation iteratively until it returns the succeeded value. To avoid exceeding the requests-per-second rate, use an interval of three to five seconds.

When the status field has the succeeded value, the JSON response includes the extracted layout, text, tables, and selection marks. The extracted data includes extracted text lines and words, bounding boxes, text appearance with handwritten indication, tables, and selection marks with selected/unselected indicated.

Handwritten classification for text lines (Latin only)

The response includes classifying whether each text line is of a handwritten style or not, along with a confidence score. This feature is supported only for Latin languages. The following example shows the handwritten classification for the text in the image.

Sample JSON output

The response to the Get Analyze Layout Result operation is a structured representation of the document with all the information extracted. See a sample document file and its structured output sample layout output.

The JSON output has two parts:

• The readResults node contains all the recognized text and selection mark. The text presentation hierarchy is page, then line, and then individual words.
• The pageResults node contains the tables and cells extracted with their bounding boxes, confidence, and a reference to the lines and words in the readResults field.

Example output

Text

The layout API extracts text from documents and images with multiple text angles and colors. It accepts photos of documents, faxes, printed and/or handwritten (English-only) text, and mixed modes. Text is extracted with information provided on lines, words, bounding boxes, confidence scores, and style (handwritten or other). All the text information is included in the readResults section of the JSON output.

Tables with headers

The Layout API extracts tables in the pageResults section of the JSON output. You can scan, photograph, or digitize documents. Tables can be complex with merged cells or columns, with or without borders, and with odd angles.

Extracted table information includes the number of columns and rows, row span, and column span. Each cell with its bounding box is output along with whether the area is recognized as part of a header or not. The model-predicted header cells can span multiple rows and aren't necessarily the first rows in a table. They also work with rotated tables. Each table cell also includes the full text with references to the individual words in the readResults section.

Selection marks (documents)

The layout API also extracts selection marks from documents. Extracted selection marks include the bounding box, confidence, and state (selected/unselected). Selection mark information is extracted in the readResults section of the JSON output.

Migration guide

• To learn how to use the v3.1 version in your applications and workflows, follow the steps in the Document Intelligence v3.1 migration guide.

• Learn how to process your own forms and documents with the Document Intelligence Studio.
• Finish a Document Intelligence quickstart, and create a document processing app in the development language of your choice.

• Find more samples on GitHub.

• Find more samples on GitHub.

• Learn how to process your own forms and documents with the Document Intelligence Sample Labeling tool.
• Finish a Document Intelligence quickstart, and create a document processing app in the development language of your choice.