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 the challenges in legacy document extraction

Many organizations, across industries, irrespective of the size of the business, deal with a large number of documents in everyday transactions. Moreover, we discussed the data diversity, data sources, and various layouts and formats for these documents in Chapter 2, Document Capture and Categorization. The data diversity at scale makes it difficult to extract elements from these documents. For example, think about a back-office task for a company. This is one of the non-mission critical tasks for a company, but at the same time, these tasks need to be fulfilled in a scheduled and timely manner. For example, the back office receives invoices at scale and needs to extract information and put it in a structured way in its enterprise resource planning (ERP) system, such as Systems, Applications, and Products (SAP) for accurate payments. Once we convert the data from unstructured documents to a structured format, a machine can...