Document Intelligence layout model

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

Document Intelligence layout model is an advanced machine-learning based document analysis API available in the Document Intelligence cloud. It enables you to take documents in various formats and return structured data representations of the documents. It combines an enhanced version of our powerful Optical Character Recognition (OCR) capabilities with deep learning models to extract text, tables, selection marks, and document structure.

Document layout analysis (v4)

Document structure layout analysis is the process of analyzing a document to extract regions of interest and their inter-relationships. 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 (v4)

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

Feature	Resources	Model ID
Layout model	• Document Intelligence Studio • REST API • C# SDK • Python SDK • Java SDK • JavaScript SDK	prebuilt-layout

Input requirements (v4)

• Supported file formats:

  Model	PDF	Image: JPEG/JPG, PNG, BMP, TIFF, HEIF	Microsoft Office: Word (DOCX), Excel (XLSX), PowerPoint (PPTX), HTML
  Read	✔	✔	✔
  Layout	✔	✔	✔
  General Document	✔	✔
  Prebuilt	✔	✔
  Custom extraction	✔	✔
  Custom classification	✔	✔	✔

• For best results, provide one clear photo or high-quality scan per document.

• For PDF and TIFF, up to 2,000 pages can be processed (with a free tier subscription, only the first two pages are processed).

• The file size for analyzing documents is 500 MB for paid (S0) tier and 4 MB for free (F0) tier.

• Image dimensions must be between 50 pixels x 50 pixels and 10,000 pixels x 10,000 pixels.

• If your PDFs are password-locked, you must remove the lock before submission.

• 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 (DPI).

• For 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.

  • For custom extraction model training, the total size of training data is 50 MB for template model and 1 GB for the neural model.

  • For 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.

Get started with Layout model

See how data, including text, tables, table headers, selection marks, and structure information is extracted from documents 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.

  

Note

Document Intelligence Studio is available with v3.0 APIs and later versions.

Sample document processed with Document Intelligence Studio

1. On the Document Intelligence Studio home page, select Layout.

2. You can analyze the sample document or upload your own files.

3. Select the Run analysis button and, if necessary, configure the Analyze options:

   

   Try Document Intelligence Studio.

Supported languages

See our Language Support—document analysis models page for a complete list of supported languages.

Data extraction (v4)

The layout model extracts text, selection marks, tables, paragraphs, and paragraph types (roles) from your documents.

Note

Document Intelligence v4.0 (2024-11-30 (GA)) and later support Microsoft office (DOCX, XLSX, PPTX) 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. 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
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 TIFF
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
HTML	Up to 3,000 characters = 1 page unit, embedded or linked images not supported	Total pages of up to 3,000 characters each

Sample code

# 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}")

View samples on GitHub.

Output

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

Extract selected pages from documents

For large multi-page 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 ascontentand 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 roles

The new machine-learning based page object detection 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:

Predicted role	Description	Supported file types
title	The main headings in the page	pdf, image, docx, pptx, xlsx, html
sectionHeading	One or more subheadings on the page	pdf, image, docx, xlsx, html
footnote	Text near the bottom of the page	pdf, image
pageHeader	Text near the top edge of the page	pdf, image, docx
pageFooter	Text near the bottom edge of the page	pdf, image, docx, pptx, html
pageNumber	Page number	pdf, image

{
    "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 pointing to the associated text. This feature applies to supported handwritten languages.

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

Sample code

# 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}")

View samples on GitHub.

Output

"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 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 addon 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.

Sample code

# 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}"
        )

View samples on GitHub.

Output

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

Tables

Extracting tables is a key requirement for processing documents containing 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 a 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 containing 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 using 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 sending it to Document Intelligence. After the analysis, post-process the pages to a single table.

• Refer to Tabular fields if you're creating custom models. Dynamic tables have a variable number of rows for each column. Fixed tables have a constant number of rows for each column.

Note

• Table analysis is not supported if the input file is XLSX.
• For 2024-11-30 (GA), the bounding regions for figures and tables cover only the core content and exclude associated caption and footnotes.

Sample code

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}'")

View samples on GitHub.

Output

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

Output 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.

Note

For v4.0 2024-11-30 (GA), the representation of tables is changed to HTML tables to enable rendering of merged cells, multi-row headers, etc. Another related change is to use Unicode checkbox characters ☒ and ☐ for selection marks instead of :selected: and :unselected:. Note that this means that the content of selection mark fields will contain :selected: even though their spans refer to Unicode characters in the top-level span.

Sample code

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,
)

View samples on GitHub.

Output

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, images) in documents play a crucial role in complementing and enhancing the textual content, providing 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 (details the text spans related to the figure, specifying 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) and caption if there's any.

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}. FigureId is included in each figure object, following an undocumented convention of {pageNumber}.{figureIndex} where figureIndex resets to one per page.

Note

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

Sample code

# 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}'")

View samples on GitHub.

Output

{
    "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 object within each section. The hierarchical structure is maintained in elements of each section. You can use output to markdown format to easily get the sections and subsections in markdown.

Sample code

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,
)

View samples on GitHub.

Output

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

Document Intelligence layout model is an advanced machine-learning based document analysis API available in the Document Intelligence cloud. It enables you to take documents in various formats and return structured data representations of the documents. It combines an enhanced version of our powerful Optical Character Recognition (OCR) capabilities with deep learning models 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 inter-relationships. 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

Input requirements

Get started with Layout model

See how data, including text, tables, table headers, selection marks, and structure information is extracted from documents 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.

Supported languages and locales

See our Language Support—document analysis models page for a complete list of supported languages.

Next steps

• Learn how to process your own forms and documents with the Document Intelligence Studio.

• Complete a Document Intelligence quickstart and get started creating a document processing app in the development language of your choice.

• Find more samples on GitHub.