Azure Form Recognizer layout model
This article applies to: Form Recognizer v3.0. Earlier version: Form Recognizer v2.1
This article applies to: Form Recognizer v2.1. Later version: Form Recognizer v3.0
Form Recognizer layout model is an advanced machine-learning based document analysis API available in the Form Recognizer 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 that text plays in a document layout:
- Geometric roles: Text, tables, and selection marks are examples of geometric roles.
- Logical roles: Titles, headings, and footers are examples of logical roles.
The following illustration shows the typical components in an image of a sample page.
Sample form processed with Form Recognizer Studio
Development options
Form Recognizer v3.0 supports the following tools:
Feature | Resources | Model ID |
---|---|---|
Layout model | prebuilt-layout |
Sample document processed with Form Recognizer Sample Labeling tool layout model:
Input requirements
For best results, provide one clear photo or high-quality scan per document.
Supported file formats:
Model PDF Image:
JPEG/JPG, PNG, BMP, and TIFFMicrosoft Office:
Word (DOCX), Excel (XLS), PowerPoint (PPT), and HTMLRead ✔ ✔ ✱ REST API version
2022/06/30-preview
Layout ✔ ✔ General Document ✔ ✔ Prebuilt ✔ ✔ Custom ✔ ✔ ✱ Microsoft Office files are currently not supported for other models or versions.
For PDF and TIFF, up to 2000 pages can be processed (with a free tier subscription, only the first two pages are processed).
The file size for analyzing documents must be less than 500 MB for paid (S0) tier and 4 MB for free (F0) tier.
Image dimensions must be between 50 x 50 pixels and 10,000 px x 10,000 pixels.
PDF dimensions are up to 17 x 17 inches, corresponding to Legal or A3 paper size, or smaller.
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 1G-MB for the neural model.
For custom classification model training, the total size of training data is
1GB
with a maximum of 10,000 pages.
- Supported file formats: JPEG, PNG, PDF, and TIFF
- For PDF and TIFF, up to 2000 pages are processed. For free tier subscribers, only the first two pages are processed.
- The file size must be less than 50 MB and dimensions at least 50 x 50 pixels and at most 10,000 x 10,000 pixels.
Try layout extraction
See how data, including text, tables, table headers, selection marks, and structure information is extracted from documents using Form Recognizer. You need the following resources:
An Azure subscription—you can create one for free
A Form Recognizer 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.
Form Recognizer Studio
Note
Form Recognizer studio is available with the v3.0 API.
Sample form processed with Form Recognizer Studio
On the Form Recognizer Studio home page, select Layout
You can analyze the sample document or select the + Add button to upload your own sample.
Select the Analyze button:
Form Recognizer Sample Labeling tool
Navigate to the Form Recognizer sample tool.
On the sample tool home page, select Use Layout to get text, tables and selection marks.
In the Form recognizer service endpoint field, paste the endpoint that you obtained with your Form Recognizer subscription.
In the key field, paste the key you obtained from your Form Recognizer resource.
In the Source field, select URL from the dropdown menu You can use our sample document:
Select the Fetch button.
Select Run Layout. The Form Recognizer Sample Labeling tool calls the Analyze Layout API and analyze the document.
View the results - see the highlighted text extracted, selection marks detected and tables detected.
Supported document types
Model | Images | TIFF | |
---|---|---|---|
Layout | ✓ | ✓ | ✓ |
Supported languages and locales
See Language Support for a complete list of supported handwritten and printed languages.
Data extraction
Starting with v3.0 GA, it extracts paragraphs and more structure information like titles, section headings, page header, page footer, page number, and footnote from the document page. These structural elements are examples of logical roles described in the previous section. This capability is supported for PDF documents and images (JPG, PNG, BMP, TIFF).
Model | Text | Selection Marks | Tables | Paragraphs | Logical roles |
---|---|---|---|---|---|
Layout | ✓ | ✓ | ✓ | ✓ | ✓ |
Supported logical roles for paragraphs: The paragraph roles are best used with unstructured documents. Paragraph roles help analyze the structure of the extracted content for better semantic search and analysis.
- title
- sectionHeading
- footnote
- pageHeader
- pageFooter
- pageNumber
Data extraction support
Model | Text | Tables | Selection marks |
---|---|---|---|
Layout | ✓ | ✓ | ✓ |
Form Recognizer v2.1 supports the following tools:
Feature | Resources |
---|---|
Layout API |
Model extraction
The layout model extracts text, selection marks, tables, paragraphs, and paragraph types (roles
) from your documents.
Paragraph extraction
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 ascontent
and 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 Form Recognizer Layout model assigns certain text blocks in the paragraphs
collection with their specialized role or type predicted by the model. They're best used 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 |
---|---|
title |
The main heading(s) in the page |
sectionHeading |
One or more subheading(s) on the page |
footnote |
Text near the bottom of the page |
pageHeader |
Text near the top edge of the page |
pageFooter |
Text near the bottom edge of the page |
pageNumber |
Page number |
{
"paragraphs": [
{
"spans": [],
"boundingRegions": [],
"role": "title",
"content": "NEWS TODAY"
},
{
"spans": [],
"boundingRegions": [],
"role": "sectionHeading",
"content": "Mirjam Nilsson"
}
]
}
Pages extraction
The pages collection is the first object you see in the service response.
"pages": [
{
"pageNumber": 1,
"angle": 0,
"width": 915,
"height": 1190,
"unit": "pixel",
"words": [],
"lines": [],
"spans": [],
"kind": "document"
}
]
Text lines and words extraction
The document layout model in Form Recognizer 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.
"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": [],
}
]
Selection marks extraction
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
). Any associated text if extracted 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
}
}
]
}
Extract tables from documents and images
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 it's 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.
{
"tables": [
{
"rowCount": 9,
"columnCount": 4,
"cells": [
{
"kind": "columnHeader",
"rowIndex": 0,
"columnIndex": 0,
"columnSpan": 4,
"content": "(In millions, except earnings per share)",
"boundingRegions": [],
"spans": []
},
]
}
]
}
Handwritten style for text lines (Latin languages only)
The response includes classifying whether each text line is of handwriting style or not, along with a confidence score. This feature is only supported for Latin languages. The following example shows an example JSON snippet.
"styles": [
{
"confidence": 0.95,
"spans": [
{
"offset": 509,
"length": 24
}
"isHandwritten": true
]
}
Annotations extraction
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
}
]
}
]
}
Barcode extraction
The Layout model extracts all identified barcodes in the barcodes
collection as a top level object under content
. Inside the content
, detected barcodes are represented as :barcode:
. Each entry in this collection represents a barcode and includes the barcode type as kind
and the embedded barcode content as value
along with its polygon
coordinates. Initially, barcodes appear at the end of each page.
Supported barcode types
Barcode Type | Example |
---|---|
QR Code |
![]() |
Code 39 |
![]() |
Code 128 |
![]() |
UPC (UPC-A & UPC-E) |
![]() |
PDF417 |
![]() |
Note
The confidence
score is hard-coded for the 2023-02-28
public preview.
"content": ":barcode:",
"pages": [
{
"pageNumber": 1,
"barcodes": [
{
"kind": "QRCode",
"value": "http://test.com/",
"span": { ... },
"polygon": [...],
"confidence": 1
}
]
}
]
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.
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 only supported for Latin languages.
Select page numbers or ranges for text extraction
For large multi-page 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 the Analyze Layout operation created. It returns a JSON response that contains a status field with the following possible values.
Field | Type | Possible values |
---|---|---|
status | string | notStarted : The analysis operation hasn't started.running : The analysis operation is in progress.failed : The analysis operation has failed.succeeded : The analysis operation has succeeded. |
Call this operation iteratively until it returns the succeeded
value. Use an interval of 3 to 5 seconds to avoid exceeding the requests per second (RPS) rate.
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 handwriting style or not, along with a confidence score. This feature is only supported 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 here for a sample document file and its structured output sample layout output.
The JSON output has two parts:
readResults
node contains all of the recognized text and selection mark. The text presentation hierarchy is page, then line, then individual words.pageResults
node contains the tables and cells extracted with their bounding boxes, confidence, and a reference to the lines and words in "readResults".
Example Output
Text
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
Layout API extracts tables in the pageResults
section of the JSON output. Documents can be scanned, photographed, or digitized. 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 information whether it's 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
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
- Follow our Form Recognizer v3.0 migration guide to learn how to use the v3.0 version in your applications and workflows.
Next steps
Learn how to process your own forms and documents with the Form Recognizer Studio
Complete a Form Recognizer quickstart and get started creating a document processing app in the development language of your choice.
Learn how to process your own forms and documents with the Form Recognizer Sample Labeling tool
Complete a Form Recognizer quickstart and get started creating a document processing app in the development language of your choice.
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