Classifier for Custom Forms and Wage Stubs

Hi Tirgar, Ashkan

Yes, you can effectively incorporate the prebuilt wage stub model into your custom classifier while avoiding the need for extensive training data. The most efficient approach is to use Azure Document Intelligence's composed models feature, which allows you to combine the prebuilt wage stub model with your custom form classifier in a single, streamlined process. This approach eliminates the need for collecting and training on thousands of wage stub variations, as the prebuilt model handles this specialized task while your custom classifier focuses on identifying other document types.

The composed models approach works by processing documents through a single API call, automatically selecting the appropriate model based on the document type. When a document is submitted, the system first attempts to identify it as a wage stub using the prebuilt model, applying a configurable confidence threshold (typically set around 0.95) to ensure accurate identification. If the document isn't confidently identified as a wage stub, it falls back to your custom classifier for processing other document types. This dual-processing system maintains reliability while significantly reducing the training data requirements.

For optimal implementation, you'll want to implement proper confidence threshold management. Set high thresholds (≥0.95) for automated processing and establish review queues for low-confidence classifications. Regular monitoring of classification accuracy will help identify areas where adjustments might be needed. Additionally, consider implementing incremental training capabilities to fine-tune your custom classifier as new document types emerge.

While an alternative approach exists using separate API calls for wage stub detection followed by custom classification, the composed models approach is recommended due to its simplicity and efficiency. It reduces operational overhead while maintaining the flexibility to handle various document types effectively. The system can be easily maintained and updated through Azure Document Intelligence's incremental training features, allowing you to adapt to changing document patterns without rebuilding the entire classification system.

Reference Links : Document Intelligence custom classification model

Hope it helps!

Thank you

Hi Ravada, thank you for the details and explanation. I am aware of composed models and how it can be used to combine Custom Extraction models and Custom Classifiers. However, I did not see any options in the Azure Document Intelligence Studio to add any Prebuilt model like Wage Stubs to the composed model. Can you please clarify how that can be achieved?

Hi Tirgar, Ashkan

Sorry for Delayed response

You're correct in observing that the Azure Document Intelligence Studio currently does not support directly adding prebuilt models—such as the Wage Stubs model—into a composed model through its graphical interface. The Studio allows you to compose multiple custom models (like custom classifiers and custom extraction models), but prebuilt models are not composable via the Studio UI.

However, you can still achieve a similar outcome programmatically using the Azure Document Intelligence REST API or SDKs. The recommended approach is to implement a two-stage classification and extraction pipeline in your application logic. In this setup, you first analyze the document using the prebuilt-payStub model. If the confidence score is high (typically ≥ 0.95), you accept the result. If not, you route the document to your custom classifier or composed model for further analysis. This method effectively integrates the prebuilt model into your workflow without needing to include it in a composed model.

This approach is particularly useful because it leverages the robustness of the prebuilt model for known document types like wage stubs, while still allowing flexibility and customization for other document types through your custom models. Microsoft also recommends this strategy in their official documentation and community forums, as it avoids the need to retrain models on formats already well-supported by prebuilt models.

Hope it helps!

Thank you