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

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: “Comprehend can take time to train your model. You can use Amazon Comprehend’s describe_document_classifier()command or check on the AWS Management Console for the completion status.”

A block of code is set as follows:

chapter2_syncdensedoc = "syncdensetext.png"
display(Image(url=s3.generate_presigned_url('get_
object', Params={'Bucket': s3BucketName, 'Key': chapter2_
syncdensedoc})))

Bold: Indicates a new term, an important word, or words that you see onscreen. For instance, words in menus or dialog boxes appear in bold. Here is an example: “The installer will present the following License Agreement screen. Click I Agree.”

Tips or Important Notes

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