Edit

Share via

Invoice processing prebuilt AI model

The invoice processing prebuilt AI model extracts key invoice data to help automate the processing of invoices. The invoice processing model is optimized to recognize common invoice elements like invoice ID, invoice date, amount due, and more.

The Invoices model allows you to augment the default behavior by building a custom Invoices model.

Use in Power Apps

Learn how to use the invoice processing prebuilt model in Power Apps in Use the invoice processing prebuilt model in Power Apps.

Use in Power Automate

Learn how to use the invoice processing prebuilt model in Power Automate in Use the invoice processing prebuilt model in Power Automate.

Supported languages and files

The following languages are supported: Albanian (Albania), Czech (Czech Republic), Chinese (simplified) China, Chinese (traditional) Hong Kong SAR, Chinese (traditional) Taiwan, Danish (Denmark), Croatian (Bosnia and Herzegovina), Croatian (Croatia), Croatian (Serbia), Dutch (Netherlands), English (Australia), English (Canada), English (India), English (United Kingdom), English (United States), Estonian (Estonia), Finnish (Finland), French (France), German (Germany), Hungarian (Hungary), Icelandic (Iceland), Italian (Italy), Japanese (Japan), Korean (Korea), Lithuanian (Lithuania), Latvian (Latvia), Malay (Malaysia), Norwegian (Norway), Polish (Poland), Portuguese (Portugal), Romanian (Romania), Slovak (Slovakia), Slovenian (Slovenia), Serbian (Serbia), Spanish (Spain), Swedish (Sweden).

To get the best results, provide one clear photo or scan per invoice.

  • The image format must be JPEG, PNG, or PDF.
  • The file size must not exceed 20 MB.
  • The image dimensions must be between 50 x 50 pixels and 10,000 x 10,000 pixels.
  • PDF dimensions must be at most 17 x 17 inches, which is the equivalent of the Legal or A3 paper sizes or smaller.
  • For PDF documents, only the first 2,000 pages are processed.

Model output

If an invoice is detected, the invoice processing model outputs the following information:

Property Definition
Amount due (text) Amount due as written on the invoice.
Amount due (number) Amount due in standardized number format. Example: 1234.98.
Confidence of amount due How confident the model is in its prediction. Score between 0 (low confidence) and 1 (high confidence).
Billing address Billing address.
Confidence of billing address How confident the model is in its prediction. Score between 0 (low confidence) and 1 (high confidence).
Billing address recipient Billing address recipient.
Confidence of billing address recipient How confident the model is in its prediction. Score between 0 (low confidence) and 1 (high confidence).
Customer address Customer address.
Confidence of customer address How confident the model is in its prediction. Score between 0 (low confidence) and 1 (high confidence).
Customer address recipient Customer address recipient.
Confidence of customer address recipient How confident the model is in its prediction. Score between 0 (low confidence) and 1 (high confidence).
Customer ID Customer ID.
Confidence of customer ID How confident the model is in its prediction. Score between 0 (low confidence) and 1 (high confidence).
Customer name Customer name.
Confidence of customer name How confident the model is in its prediction. Score between 0 (low confidence) and 1 (high confidence).
Customer tax ID The taxpayer number associated with the customer.
Confidence of customer tax ID How confident the model is in its prediction. Score between 0 (low confidence) and 1 (high confidence).
Due date (text) Due date as written on the invoice.
Due date (date) Due date in standardized date format. Example: 2019-05-31.
Confidence of due date How confident the model is in its prediction. Score between 0 (low confidence) and 1 (high confidence).
Invoice date (text) Invoice date as written on the invoice.
Invoice date (date) Invoice date in standardized date format. Example: 2019-05-31.
Confidence of invoice date How confident the model is in its prediction. Score between 0 (low confidence) and 1 (high confidence).
Invoice ID Invoice ID.
Confidence of invoice ID How confident the model is in its prediction. Score between 0 (low confidence) and 1 (high confidence).
Invoice total (text) Invoice total as written on the invoice.
Invoice total (number) Invoice total in standardized date format. Example: 2019-05-31.
Confidence of invoice total How confident the model is in its prediction. Score between 0 (low confidence) and 1 (high confidence).
Line Items The line items extracted from the invoice. Confidence scores are available for each column.
  • Line item amount: Amount for a line item. Returned in text and number format.
  • Line item description: Description for a line item. Returned in text format.
  • Line item quantity: Quantity for a line item. Returned in text and number format.
  • Line item unit price: Unit price for a line item. Returned in text and number format.
  • Line item product code: Product code for a line item. Returned in text format.
  • Line item unit: Unit for a line item (for example, kg and lb). Returned in text format.
  • Line item date: Date for a line item. Returned in text and date format.
  • Line item tax: Tax for a line item. Returned in text and number format.
  • Line item all columns: Returns all the columns from the line item as a line of text.
Payment terms The terms of payment for the invoice.
Confidence of payment terms How confident the model is in its prediction. Score between 0 (low confidence) and 1 (high confidence).
Purchase order Purchase order.
Confidence of purchase order How confident the model is in its prediction. Score between 0 (low confidence) and 1 (high confidence).
Previous unpaid balance (text) Previous unpaid balance as written on the invoice.
Previous unpaid balance (number) Previous unpaid balance in standardized number format. Example: 1234.98.
Confidence of previous unpaid balance How confident the model is in its prediction. Score between 0 (low confidence) and 1 (high confidence).
Remittance address Remittance address.
Confidence of remittance address How confident the model is in its prediction. Score between 0 (low confidence) and 1 (high confidence).
Remittance address recipient Remittance address recipient.
Confidence of remittance address recipient How confident the model is in its prediction. Score between 0 (low confidence) and 1 (high confidence).
Service address Service address.
Confidence of service address How confident the model is in its prediction. Score between 0 (low confidence) and 1 (high confidence).
Service address recipient Service address recipient.
Confidence of service address recipient How confident the model is in its prediction. Score between 0 (low confidence) and 1 (high confidence).
Service start date (text) Service start date as written on the invoice.
Service start date (date) Service start date in standardized date format. Example: 2019-05-31.
Confidence of service start date How confident the model is in its prediction. Score between 0 (low confidence) and 1 (high confidence).
Service end date (text) Service end date as written on the invoice.
Service end date (date) Service end date in standardized date format. Example: 2019-05-31.
Confidence of service end date How confident the model is in its prediction. Score between 0 (low confidence) and 1 (high confidence).
Shipping address Shipping address.
Confidence of shipping address How confident the model is in its prediction. Score between 0 (low confidence) and 1 (high confidence).
Shipping address recipient Shipping address recipient.
Confidence of shipping address recipient How confident the model is in its prediction. Score between 0 (low confidence) and 1 (high confidence).
Subtotal (text) Subtotal as written on the invoice.
Subtotal (number) Subtotal in standardized number format. Example: 1234.98.
Confidence of subtotal How confident the model is in its prediction. Score between 0 (low confidence) and 1 (high confidence).
Total tax (text) Total tax as written on the invoice.
Total tax (number) Total tax in standardized number format. Example: 1234.98.
Confidence of total tax How confident the model is in its prediction. Score between 0 (low confidence) and 1 (high confidence).
Vendor address Vendor address.
Confidence of vendor address How confident the model is in its prediction. Score between 0 (low confidence) and 1 (high confidence).
Vendor address recipient Vendor address recipient.
Confidence of vendor address recipient How confident the model is in its prediction. Score between 0 (low confidence) and 1 (high confidence).
Vendor name Vendor name.
Confidence of vendor name How confident the model is in its prediction. Score between 0 (low confidence) and 1 (high confidence).
Vendor tax ID The taxpayer number associated with the vendor.
Confidence of vendor tax ID How confident the model is in its prediction. Score between 0 (low confidence) and 1 (high confidence).
Detected text Line of recognized text from running OCR on an invoice. Returned as a part of a list of text.
Detected key Key-value pairs are all the identified labels or keys and their associated responses or values. You can use these to extract additional values that aren't part of the predefined list of fields.
Detected value Key-value pairs are all the identified labels or keys and their associated responses or values. You can use these to extract additional values that aren't part of the predefined list of fields.