Document Intelligence business card model
Important
Starting with Document Intelligence v4.0 (preview), and going forward, the business card model (prebuilt-businessCard) is deprecated. To extract data from business card formats, use the following:
Feature | version | Model ID |
---|---|---|
Business card model | • v3.1:2023-07-31 (GA) • v3.0:2022-08-31 (GA) • v2.1 (GA) |
prebuilt-businessCard |
This content applies to: v3.0 (GA) | Latest versions:
v4.0 (preview)
v3.1 | Previous version:
v2.1
This content applies to: v2.1 | Latest version:
v4.0 (preview)
The Document Intelligence 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, surname, 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 Document Intelligence Studio
Sample business processed with Document Intelligence Sample Labeling tool
Development options
Document Intelligence v3.1:2023-07-31 (GA) supports the following tools, applications, and libraries:
Feature | Resources | Model ID |
---|---|---|
Business card model | • Document Intelligence Studio • REST API • C# SDK • Python SDK • Java SDK • JavaScript SDK |
prebuilt-businessCard |
Document Intelligence v3.0:2022-08-31 (GA) supports the following tools, applications, and libraries:
Feature | Resources | Model ID |
---|---|---|
Business card model | • Document Intelligence Studio • REST API • C# SDK • Python SDK • Java SDK • JavaScript SDK |
prebuilt-businessCard |
Document Intelligence v2.1 (GA) supports the following tools, applications, and libraries:
Feature | Resources |
---|---|
Business card model | • Document Intelligence labeling tool • REST API • Client-library SDK • Document Intelligence Docker container |
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 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.
Document Intelligence Studio
Note
Document Intelligence Studio is available with v3.1 and v3.0 APIs.
On the Document Intelligence Studio home page, select Business cards.
You can analyze the sample business card or upload your own files.
Select the Run analysis button and, if necessary, configure the Analyze options :
Document Intelligence Sample Labeling tool
Navigate to the Document Intelligence Sample Tool.
On the sample tool home page, select the Use prebuilt model to get data tile.
Select the Form Type to analyze from the dropdown menu.
Choose a URL for the file you would like to analyze from the below options:
In the Source field, select URL from the dropdown menu, paste the selected URL, and select the Fetch button.
In the Document Intelligence service endpoint field, paste the endpoint that you obtained with your Document Intelligence subscription.
In the key field, paste the key you obtained from your Document Intelligence resource.
Select Run analysis. The Document Intelligence Sample Labeling tool calls the Analyze Prebuilt API and analyze the document.
View the results - see the key-value pairs extracted, line items, highlighted text extracted, and tables detected.
Note
The Sample Labeling tool does not support the BMP file format. This is a limitation of the tool not the Document Intelligence Service.
Input requirements
Supported file formats:
Model PDF Image: JPEG/JPG
,PNG
,BMP
,TIFF
,HEIF
Microsoft Office:
Word (DOCX
), Excel (XLSX
), PowerPoint (PPTX
), HTMLRead ✔ ✔ ✔ Layout ✔ ✔ ✔ (2024-07-31-preview, 2024-02-29-preview, 2023-10-31-preview) General Document ✔ ✔ Prebuilt ✔ ✔ Custom extraction ✔ ✔ Custom classification ✔ ✔ ✔ (2024-07-31-preview, 2024-02-29-preview) 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 2024-07-31-preview and later, the total size of training data is2
GB with a maximum of 10,000 pages.
- The supported file formats: JPEG, PNG, PDF, and TIFF
- PDF and TIFF, up to 2,000 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
See our Language Support page for a complete list of supported languages.
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 | |
Departments | Array of strings | Department or organization of contact | |
JobTitles | Array of strings | Listed Job title of contact | |
Emails | Array of strings | Contact email address | |
Websites | Array of strings | Company website | |
Addresses | Array of strings | Address extracted from business card | |
MobilePhones | Array of phone numbers | Mobile phone number from business card | +1 xxx xxx xxxx |
Faxes | Array of phone numbers | Fax phone number from business card | +1 xxx xxx xxxx |
WorkPhones | Array of phone numbers | Work phone number from business card | +1 xxx xxx xxxx |
OtherPhones | Array of phone numbers | Other phone number 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, Washington 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 v3.1
- Follow our Document Intelligence v3.1 migration guide to learn how to use the v3.0 version in your applications and workflows.
Next steps
Try processing 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.
Try processing your own forms and documents with the Document Intelligence Sample Labeling tool
Complete a Document Intelligence quickstart and get started creating a document processing app in the development language of your choice.
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