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Azure Invoice Recognizer

azure-invoice-model

Applies advanced machine learning to accurately extract from forms and tables in documents

The invoice model combines powerful Optical Character Recognition (OCR) capabilities with deep learning models to analyze and extract key fields and line items from sales invoices. Invoices can be of various formats and quality including phone-captured images, scanned documents, and digital PDFs. The API analyzes invoice text; extracts key information such as customer name, billing address, due date, and amount due; and returns a structured JSON data representation.

You can list a component in the marketplace, and define if you want it to be a template.

✅ Available in Marketplace

❌ Can not be used as a template

What is a Model?

This component is a model, which is a type of store that is specialized for handling AI/ML model storage, which includes both the implementation, and the results of training.

Models are a foundational part of Kodexa, and are used in many different ways. For example, a model can be used to classify documents, or to extract data from documents. Models can also be used to train other models.

Metadata

✅ Atomic Deployment (Recommended)

❌ Not trainable

Model Runtime

A model needs to reference a model runtime to use.

✅ kodexa/base-model-runtime

The model also has the following model runtime parameters configured. This influences how the model is run, see the model runtime references to determine what parameters are available.

Parameter Name Value
module azure_models
function invoice_infer

Inference Options

When you use the model for inference, you can use the following options:

Option Name Default Required? Type Description
store_azure_output False None boolean Store the JSON response from Azure in the document as a feature on the root node
keep_azure_lines True None boolean Retain the line layout from Azure in the document

Model Label Taxonomy

This model provides a taxonomy of labels that can be applied, these labels are not used to extract data but are "meta-labels" that are in place to help the model train on the content.

🏷️ Azure Form Recognizer

🏷️ Customer Name

🏷️ Customer ID

🏷️ Purchase Order

🏷️ Invoice ID

🏷️ Invoice Date

🏷️ Due Date

🏷️ Vendor Name

🏷️ Vendor Address

🏷️ Vendor Address Recipient

🏷️ Customer Address

🏷️ Customer Address Recipient

🏷️ Billing Address

🏷️ Billing Address Recipient

🏷️ Shipping Address

🏷️ Shipping Address Recipient

🏷️ Sub Total

🏷️ Total Tax

🏷️ Invoice Total

🏷️ Previous Unpaid Balance

🏷️ Amount Due

🏷️ Service Start Date

🏷️ Service End Date

🏷️ Service Address

🏷️ Service Address Recipient

🏷️ Remittance Address

🏷️ Remittance Address Recipient

🏷️ Items

🏷️ Description

🏷️ Amount