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Azure Form Recognizer business card model

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This article applies to: Form Recognizer v3.0 checkmark Form Recognizer v3.0. Earlier version: Form Recognizer v2.1

This article applies to: Form Recognizer v2.1 checkmark Form Recognizer v2.1. Later version: Form Recognizer v3.0

The Form Recognizer business card model combines powerful Optical Character Recognition (OCR) capabilities with deep learning models to analyze and extract data from business card images. The API analyzes printed business cards; extracts key information such as first name, last name, company name, email address, and phone number; and returns a structured JSON data representation.

Business card data extraction

Business cards are a great way to represent a business or a professional. The company logo, fonts and background images found in business cards help promote the company branding and differentiate it from others. Applying OCR and machine-learning based techniques to automate scanning of business cards is a common image processing scenario. Enterprise systems used by sales and marketing teams typically have business card data extraction capability integration into for the benefit of their users.

Sample business card processed with Form Recognizer Studio

Screenshot of a sample business card analyzed in the Form Recognizer Studio.

Sample business processed with Form Recognizer Sample Labeling tool

Screenshot of a sample business card analyzed with the Form Recognizer Sample Labeling tool.

Development options

Form Recognizer v3.0 supports the following tools:

Feature Resources Model ID
Business card model prebuilt-businessCard

Form Recognizer v2.1 supports the following tools:

Feature Resources
Business card model

Try business card data extraction

See how data, including name, job title, address, email, and company name, is extracted from business cards. 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.

Screenshot: keys and endpoint location in the Azure portal.

Form Recognizer Studio

Note

Form Recognizer studio is available with the v3.0 API.

  1. On the Form Recognizer Studio home page, select Business cards

  2. You can analyze the sample business card or select the + Add button to upload your own sample.

  3. Select the Analyze button:

    Screenshot: analyze business card menu.

    Try Form Recognizer Studio

Form Recognizer Sample Labeling tool

  1. Navigate to the Form Recognizer Sample Tool.

  2. On the sample tool home page, select the Use prebuilt model to get data tile.

    Screenshot of the layout model analyze results operation.

  3. Select the Form Type to analyze from the dropdown menu.

  4. Choose a URL for the file you would like to analyze from the below options:

  5. In the Source field, select URL from the dropdown menu, paste the selected URL, and select the Fetch button.

    Screenshot of source location dropdown menu.

  6. In the Form recognizer service endpoint field, paste the endpoint that you obtained with your Form Recognizer subscription.

  7. In the key field, paste the key you obtained from your Form Recognizer resource.

    Screenshot of the select-form-type dropdown menu.

  8. Select Run analysis. The Form Recognizer Sample Labeling tool calls the Analyze Prebuilt API and analyze the document.

  9. View the results - see the key-value pairs extracted, line items, highlighted text extracted and tables detected.

    Screenshot of the business card model analyze results operation.

Note

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

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 TIFF    		 Microsoft Office:
    Word (DOCX), Excel (XLS), PowerPoint (PPT), and HTML
    Read ✔ ✔ ✱ 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.

Supported languages and locales

Note

It's not necessary to specify a locale. This is an optional parameter. The Form Recognizer deep-learning technology will auto-detect the language of the text in your image.

Model Language—Locale code Default
Business card (v3.0 API)
  • English (United States)—en-US
  • English (Australia)—en-AU
  • English (Canada)—en-CA
  • English (United Kingdom)—en-GB
  • English (India)—en-IN
  • English (Japan)—en-JP
  • Japanese (Japan)—ja-JP
 Autodetected (en-US or ja-JP)
Business card (v2.1 API)
  • English (United States)—en-US
  • English (Australia)—en-AU
  • English (Canada)—en-CA
  • English (United Kingdom)—en-GB
  • English (India)—en-IN
 Autodetected

Field extractions

Name Type Description Standardized output
ContactNames Array of objects Contact name
FirstName String First (given) name of contact
LastName String Last (family) name of contact
CompanyNames Array of strings Company name(s)
Departments Array of strings Department(s) or organization(s) of contact
JobTitles Array of strings Listed Job title(s) of contact
Emails Array of strings Contact email address(es)
Websites Array of strings Company website(s)
Addresses Array of strings Address(es) extracted from business card
MobilePhones Array of phone numbers Mobile phone number(s) from business card +1 xxx xxx xxxx
Faxes Array of phone numbers Fax phone number(s) from business card +1 xxx xxx xxxx
WorkPhones Array of phone numbers Work phone number(s) from business card +1 xxx xxx xxxx
OtherPhones Array of phone numbers Other phone number(s) from business card +1 xxx xxx xxxx

Fields extracted

Name Type Description Text
ContactNames array of objects Contact name extracted from business card [{ "FirstName": "John", "LastName": "Doe" }]
FirstName string First (given) name of contact "John"
LastName string Last (family) name of contact "Doe"
CompanyNames array of strings Company name extracted from business card ["Contoso"]
Departments array of strings Department or organization of contact ["R&D"]
JobTitles array of strings Listed Job title of contact ["Software Engineer"]
Emails array of strings Contact email extracted from business card ["johndoe@contoso.com"]
Websites array of strings Website extracted from business card ["https://www.contoso.com"]
Addresses array of strings Address extracted from business card ["123 Main Street, Redmond, WA 98052"]
MobilePhones array of phone numbers Mobile phone number extracted from business card ["+19876543210"]
Faxes array of phone numbers Fax phone number extracted from business card ["+19876543211"]
WorkPhones array of phone numbers Work phone number extracted from business card ["+19876543231"]
OtherPhones array of phone numbers Other phone number extracted from business card ["+19876543233"]

Supported locales

Prebuilt business cards v2.1 supports the following locales:

  • en-us
  • en-au
  • en-ca
  • en-gb
  • en-in

Migration guide and REST API v3.0

Next steps