Understanding Documents thanks to RPAI (Part 2)

The summer vacation was great, but it is also exciting to be back and kick off the first blog entry after the break...

In the first part of the Document Understanding (DU), I have mainly focused on the "Rule Based" approach and also promised to go into details with the "Document Understanding Framework". 

Let's start with it !

DU framework makes it simpler to structure the DU flow that you build in Studio. You can get more details about it here under the "Framework Components".

I will not go into details about the components since they are explained in details on the above link I provided, but still want to refer to the two important loops seen on the picture.

The first one is about the "Validation of the Classification" as you can help the robot do the classification via "Classification Station" if you get an escalation/task about it through Action Center. 

Assume that you want to process three different documents in your workflow:

• Invoices
• Receipts
• Purchase Orders

Since you will most probably want to fetch different fields from those documents, the DU automation should be able to separate those documents, shortly they need to be classified correctly. You can configure this so that the robot creates a task if it is not 100% sure about the classification. Once you get the notification, you can go to the Action Center to help the robot by dragging and dropping the documents into the right areas should the robot has failed to do so. If you have used "ML Classifier" in your workflow, you can train the model with your input and make sure that it does not do the same mistakes again.

The second one I would like to refer to is the second loop on the framework picture, which is about the "Validation of the Extraction". This is where you help the robot again through the Action Center when you get a new task via "Validation Station", about the retrieved fields from the document. Again the input you give back to the ML model, assuming you have built the flow with it, can be used to train the model so that it can fetch the fields correctly next time.

Within the ML context, there is also one important component that is not part of the framework above, called "Data Manager". This is where you pre-train a DU related ML model. You can read more about it on this link.

Hopefully this gives you an idea on how you can build a DU solution by using automation. Feel free to add comments or questions below this entry. 

In the next blog post, we will deep dive on how to create an ML model and how to train it.

Until next time, have great and healthy days !