Document Intelligence credit card model

This content applies to: v4.0 (preview)

The Document Intelligence credit/debit card model uses powerful Optical Character Recognition (OCR) capabilities to analyze and extract key fields from credit and debit cards. Credit cards and debit cards can be of various formats and quality including phone-captured images, scanned documents, and digital PDFs. The API analyzes document text; extracts key information such as Card Number, Issuing Bank, and Expiration Date; and returns a structured JSON data representation. The model currently supports English-language document formats.

Automated card processing

Automated Credit/Debit card processing is the process of extracting key fields from bank cards. Historically, bank card analysis process is achieved manually and, hence, very time consuming. Accurate extraction of key data from bank cards s is typically the first and one of the most critical steps in the contract automation process.

Development options

Document Intelligence C supports the following tools, applications, and libraries:

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

Input requirements

• Supported file formats:

  Model	PDF	Image: JPEG/JPG, PNG, BMP, TIFF, HEIF	Microsoft Office: Word (DOCX), Excel (XLSX), PowerPoint (PPTX), HTML
  Read	✔	✔	✔
  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 is 2 GB with a maximum of 10,000 pages.

Try credit card data extraction

To see how data extraction works for the Credit/Debit card service, 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

1. On the Document Intelligence Studio home page, select Credit/Debit Card.

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

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

   

Try Document Intelligence Studio

Supported languages and locales

For a complete list of supported languages, see our prebuilt model language support page.

Field extraction

• For supported document extraction fields, refer to the credit card model schema page in our GitHub sample repository.

• The bank cards key-value pairs and line items extracted are in the documentResults section of the JSON output.

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.