Form Recognizer models

Azure Form Recognizer supports a wide variety of models that enable you to add intelligent document processing to your apps and flows. You can use a prebuilt document analysis or domain specific model or train a custom model tailored to your specific business needs and use cases. Form Recognizer can be used with the REST API or Python, C#, Java, and JavaScript SDKs.

Model overview

Model	Description
Document analysis
🆕Read (preview)	Extract typeface and handwritten text lines, words, locations, and detected languages.
🆕General document (preview)	Extract text, tables, structure, key-value pairs, and named entities.
Layout	Extract text and layout information from documents.
Prebuilt
🆕W-2 (preview)	Extract employee, employer, wage information, etc. from US W-2 forms.
Invoice	Extract key information from English and Spanish invoices.
Receipt	Extract key information from English receipts.
ID document	Extract key information from US driver licenses and international passports.
Business card	Extract key information from English business cards.
Custom
Custom	Extract data from forms and documents specific to your business. Custom models are trained for your distinct data and use cases.
Composed	Compose a collection of custom models and assign them to a single model built from your form types.

Read (preview)

The Read API analyzes and extracts ext lines, words, their locations, detected languages, and handwritten style if detected.

Sample document processed using the Form Recognizer Studio:

Learn more: read model

W-2 (preview)

The W-2 model analyzes and extracts key information reported in each box on a W-2 form. The model supports standard and customized forms from 2018 to the present, including single and multiple forms on one page.

Sample W-2 document processed using Form Recognizer Studio:

Learn more: W-2 model

General document (preview)

• The general document API supports most form types and will analyze your documents and associate values to keys and entries to tables that it discovers. It's ideal for extracting common key-value pairs from documents. You can use the general document model as an alternative to training a custom model without labels.

• The general document is a pre-trained model and can be directly invoked via the REST API.

• The general document model supports named entity recognition (NER) for several entity categories. NER is the ability to identify different entities in text and categorize them into pre-defined classes or types such as: person, location, event, product, and organization. Extracting entities can be useful in scenarios where you want to validate extracted values. The entities are extracted from the entire content.

Sample document processed using the Form Recognizer Studio:

Learn more: general document model

Layout

The Layout API analyzes and extracts text, tables and headers, selection marks, and structure information from documents.

Sample document processed using the Form Recognizer Studio:

Learn more: layout model

Invoice

The invoice model analyzes and extracts key information from sales invoices. The API analyzes invoices in various formats and extracts key information such as customer name, billing address, due date, and amount due. Currently, the model supports English, Spanish, German, French, Italian, Portuguese, and Dutch invoices.

Sample invoice processed using Form Recognizer Studio:

Learn more: invoice model

Receipt

• The receipt model analyzes and extracts key information from printed and handwritten sales receipts.

• The preview version v3.0 also supports single-page hotel receipt processing.

Sample receipt processed using Form Recognizer Studio:

Learn more: receipt model

ID document

The ID document model analyzes and extracts key information from the following documents:

• U.S. Driver's Licenses (all 50 states and District of Columbia)

• Biographical pages from international passports (excluding visa and other travel documents). The API analyzes identity documents and extracts

Sample U.S. Driver's License processed using Form Recognizer Studio:

Learn more: identity document model

Business card

The business card model analyzes and extracts key information from business card images.

Sample business card processed using Form Recognizer Studio:

Learn more: business card model

Custom

• Custom models analyze and extract data from forms and documents specific to your business. The API is a machine-learning program trained to recognize form fields within your distinct content and extract key-value pairs and table data. You only need five examples of the same form type to get started and your custom model can be trained with or without labeled datasets.

• The preview version v3.0 custom model supports signature detection in custom forms (template model) and cross-page tables in both template and neural models.

Sample custom template processed using Form Recognizer Studio:

Learn more: custom model

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.

Composed model dialog window in Form Recognizer Studio:

Learn more: custom model

Model data extraction

Model ID	Text extraction	Language detection	Selection Marks	Tables	Paragraphs	Paragraph roles	Key-Value pairs	Fields
🆕 prebuilt-read	✓	✓			✓
🆕 prebuilt-tax.us.w2	✓		✓		✓			✓
🆕 prebuilt-document	✓		✓	✓	✓		✓
prebuilt-layout	✓		✓	✓	✓	✓
prebuilt-invoice	✓		✓	✓	✓		✓	✓
prebuilt-receipt	✓				✓			✓
prebuilt-idDocument	✓				✓			✓
prebuilt-businessCard	✓				✓			✓
Custom	✓		✓	✓	✓			✓

Input requirements

• For best results, provide one clear photo or high-quality scan per document.
• Supported file formats: JPEG/JPG, PNG, BMP, TIFF, and PDF (text-embedded or scanned). Additionally, the Read API supports Microsoft Word (DOCX), Excel (XLS), PowerPoint (PPT), and HTML files.
• 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 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 x 10,000 pixels.
• The total size of the training data is 500 pages or less.
• If your PDFs are password-locked, you must remove the lock before submission.

Note

The Sample Labeling tool does not support the BMP file format. This is a limitation of the tool not the Form Recognizer Service.

Version migration

Learn how to use Form Recognizer v3.0 in your applications by following our Form Recognizer v3.0 migration guide

Next steps

• Learn how to process your own forms and documents with our Form Recognizer sample tool

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