Summary

In this chapter, we covered two things using a reference architecture as well as a code walkthrough. Firstly, we covered how you can extract data from various types of documents, such as pay stubs, bank statements, or identification cards using Amazon Textract. Then, we learned how you can perform some post-processing to create a labeled training file for Amazon Comprehend custom classification training.

We showed you that even with 36 bank statement documents and 24 pay stubs as a training sample, you can achieve really good accuracy using Amazon Comprehend transfer-learning capabilities and AutoML with document or text classification. Obviously, the accuracy improves with more data.

Then, you learned how to set up a training job in the AWS Management Console and how to set up a real-time classification endpoint using the AWS Management Console.

Secondly, you learned how you can set up humans in the loop with the real-time classification endpoint to review/verify...