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)
Part 1: Accurate Extraction of Documents and Categorization
Part 2: Enrichment of Data and Post-Processing of Data
Part 3: Intelligent Document Processing in Industry Use Cases

Learning post-processing for a completeness check

Before diving deeper into the implementation of how to use post-processing for a completeness check, first, let’s understand the requirements of the Review and Verification stage of the IDP pipeline. In the previous stages of IDP, we discussed how to extract data from documents. Looking inside the documents, we can validate that the key fields needed to process documents meet the accuracy standards set by the business requirements. Most of the time, business uses simple business rules such as whether key fields such as Name or ID are not empty. For example, in a claims form, you want to make sure the Insured ID is always filled in for it to be processed promptly. While we used relatively simple rules in the previous example, you have the ability to construct more complex rules based on your business needs – as an example, reimbursement of over $100,000 (or for X amount) always requires human attention or an additional...