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

Understanding document enrichment

To get insights and business value from your documents, you will need to understand dynamic topics and document attributes. You also have a requirement to augment documents, such as redacting sensitive information, translating the extracted data to an additional language as per your needs, or just augmenting documents with inferred metadata. Getting these additional insights is known as the document enrichment stage of the IDP pipeline. During the document enrichment stage, you augment and append your existing document insights with business- or domain-specific context from additional sources. Let’s discuss a couple of examples of document enrichment.

For example, your need is to translate documents from English to Spanish to support global customers. You can leverage AWS AI services such as Amazon Translate to translate documents from one language to another. We will use the following architecture for the translation of text in a document...