What type of model should be used in Form Recognizer for processing receipts?

The prebuilt receipts model in Form Recognizer is specifically designed for processing receipts. This model utilizes advanced machine learning techniques to efficiently extract key information typically found in receipts, such as vendor name, total amount, transaction date, and item details, without the need for extensive customization or training on specific dataset types.

Using the prebuilt receipts model is advantageous because it dramatically reduces development time and complexity. Since the model is already trained on a diverse range of receipt formats, users can get accurate results without having to curate their own dataset or develop a custom machine learning solution. This model is ideal for scenarios where standard receipt processing capabilities are required, making it a perfect fit for most applications dealing with receipts.

In contrast, custom models would require training on labeled receipt data and might not be necessary for standard receipt formats that the prebuilt model can handle efficiently. Layout models focus on extracting structural information from documents rather than specific data fields relevant for receipts, while analysis models generally deal with more complex tasks that may not be directly applicable to straightforward receipt processing.