Document Intelligence mortgage document models
This content applies to: v4.0 (preview)
The Document Intelligence Mortgage models use powerful Optical Character Recognition (OCR) capabilities and deep learning models to analyze and extract key fields from mortgage documents. Mortgage documents can be of various formats and quality. The API analyzes mortgage documents and returns a structured JSON data representation. The models currently support English-language documents only.
Supported document types:
- Uniform Residential Loan Application (Form 1003)
- Uniform Underwriting and Transmittal Summary (Form 1008)
- Closing Disclosure form
Development options
Document Intelligence v4.0 (2024-02-29-preview) supports the following tools, applications, and libraries:
Feature | Resources | Model ID |
---|---|---|
Mortgage model | • Document Intelligence Studio • REST API • C# SDK • Python SDK • Java SDK • JavaScript SDK |
• prebuilt-mortgage.us.1003 • prebuilt-mortgage.us.1008 • prebuilt-mortgage.us.closingDisclosure |
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, TIFF, HEIFMicrosoft Office:
Word (DOCX), Excel (XLSX), PowerPoint (PPTX), and HTMLRead ✔ ✔ ✔ Layout ✔ ✔ ✔ (2024-02-29-preview, 2023-10-31-preview) General Document ✔ ✔ Prebuilt ✔ ✔ Custom extraction ✔ ✔ Custom classification ✔ ✔ ✔ (2024-02-29-preview) 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 is 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.
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.
Try mortgage documents data extraction
To see how data extraction works for the mortgage documents 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
On the Document Intelligence Studio home page, select mortgage.
You can analyze the sample mortgage documents or upload your own files.
Select the Run analysis button and, if necessary, configure the Analyze options:
Supported languages and locales
See our Language Support—prebuilt models page for a complete list of supported languages.
Field extraction 1003 Uniform Residential Loan Application (URLA)
The following are the fields extracted from a 1003 URLA form in the JSON output response.
Name | Type | Description | Example output |
---|---|---|---|
LenderLoanNumber | String | Lender loan number or universal loan identifier | 10Bx939c5543TqA1144M999143X38 |
AgencyCaseNumber | String | Agency case number | 115894 |
Borrower | Object | An object that contains the borrower's identity markers such as name, SSN, birth date. | |
Co-Borrower | Object | An object that contains the Co-Borrower's names, and signed date. | |
CurrentEmployment | Object | An Object that contains information about the current employment including: Employer name, Employer Phone number, Employer address. | |
Loan | Object | An object that contains loan information including: amount, purpose type, refinance type. | |
Property | object | An object that contains information about the property including: address, number of units, value. |
The 1003 URLA key-value pairs and line items extracted are in the documentResults
section of the JSON output.
Field extraction 1008 Uniform Underwriting and Transmittal Summary
The following are the fields extracted from a 1008 form in the JSON output response.
Name | Type | Description | Example output |
---|---|---|---|
Borrower | Object | An object that contains information about the borrower including: name, and number of borrowers. | |
Property | Object | An object that contains information about the property including: address, occupancy status, sales price. | |
Mortgage | Object | An object that contains information about the mortgage including: Loan type, amortization type, loan purpose type. | |
Underwriting | Object | An object that contains information about the underwriting information including: underwriter name, appraiser name, borrower income. | |
Seller | Object | An object that contains information about the seller including: Name, address, number. |
The form 1008 key-value pairs and line items extracted are in the documentResults
section of the JSON output.
Field extraction mortgage closing disclosure
The following are the fields extracted from a mortgage closing disclosure form in the JSON output response.
Name | Type | Description | Example output |
---|---|---|---|
Closing | Object | An object that contains information about the closing information including: Issue date, Closing date, Disbursement date. | |
Transaction | Object | An object that contains information about the transaction information including: Borrowers name, Borrowers address, Seller name. | |
Loan | Object | An object that contains loan information including: term, purpose, product. |
The mortgage closing disclosure 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.
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