Book Image

Intelligent Document Processing with AWS AI/ML

By : Sonali Sahu
Book Image

Intelligent Document Processing with AWS AI/ML

By: Sonali Sahu

Overview of this book

With the volume of data growing exponentially in this digital era, it has become paramount for professionals to process this data in an accelerated and cost-effective manner to get value out of it. Data that organizations receive is usually in raw document format, and being able to process these documents is critical to meeting growing business needs. This book is a comprehensive guide to helping you get to grips with AI/ML fundamentals and their application in document processing use cases. You’ll begin by understanding the challenges faced in legacy document processing and discover how you can build end-to-end document processing pipelines with AWS AI services. As you advance, you'll get hands-on experience with popular Python libraries to process and extract insights from documents. This book starts with the basics, taking you through real industry use cases for document processing to deliver value-based care in the healthcare industry and accelerate loan application processing in the financial industry. Throughout the chapters, you'll find out how to apply your skillset to solve practical problems. By the end of this AWS book, you’ll have mastered the fundamentals of document processing with machine learning through practical implementation.
Table of Contents (16 chapters)
1
Part 1: Accurate Extraction of Documents and Categorization
6
Part 2: Enrichment of Data and Post-Processing of Data
10
Part 3: Intelligent Document Processing in Industry Use Cases

Summary

In this chapter, we discussed core features of Amazon Comprehend, including the extraction of pre-trained entities such as “Person,” “Date,” and “Location” from text. We then discussed how we can leverage Amazon Comprehend for the document extraction stage of the IDP pipeline. We also discussed how to use Amazon Textract to extract text from a document and pass it to Amazon Comprehend for entity extraction.

We then reviewed the need for custom entities extraction and how to train your own Comprehend custom entity recognizer model. We discussed the two-step process of training a custom entity recognizer and then created an analysis job for custom entities extraction from any type of document.

In the next chapter, we will extend the extraction and enrichment stage of the IDP pipeline using Amazon Comprehend Medical. You will be introduced to the enrichment stage of IDP and discover how to leverage Amazon Comprehend to enrich your...