Intelligent Document Processing with AWS AI/ML: A comprehensive guide to building IDP pipelines with applications across industries

Length: 246 pages
Edition: 1
Language: English
Publisher: Packt Publishing
Publication Date: 2022-10-21
ISBN-10: 1801810567
ISBN-13: 9781801810562
Sales Rank: #683710 (See Top 100 Books)

Build real-world artificial intelligence applications across industries with the help of intelligent document processing

Key Features

Tackle common document processing problems to extract value from any type of document
Unlock deeper levels of insights on IDP in a more structured and accelerated way using AWS AI/ML
Apply your knowledge to solve real document analysis problems in various industry applications

Book Description

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.

What you will learn

Understand the requirements and challenges in deriving insights from a document
Explore common stages in the intelligent document processing pipeline
Discover how AWS AI/ML can successfully automate IDP pipelines
Find out how to write clean and elegant Python code by leveraging AI
Get to grips with the concepts and functionalities of AWS AI services
Explore IDP across industries such as insurance, healthcare, finance, and the public sector
Determine how to apply business rules in IDP
Build, train, and deploy models with serverless architecture for IDP

Who this book is for

This book is for technical professionals and thought leaders who want to understand and solve business problems by leveraging insights from their documents. If you want to learn about machine learning and artificial intelligence, and work with real-world use cases such as document processing with technology, this book is for you. To make the most of this book, you should have basic knowledge of AI/ML and python programming concepts. This book is also especially useful for developers looking to explore AI/ML with industry use cases.

Intelligent Document Processing with AWS AI/ML
Contributors
About the authors
About the reviewer
Preface
    Who this book is for
    What this book covers
    To get the most out of this book
    Download the example code files
    Download the color images
    Conventions used
    Get in touch
    Share your thoughts
Part 1: Accurate Extraction of Documents and Categorization
Chapter 1: Intelligent Document Processing with AWS AI and ML
    Understanding common document processing use cases across industries
    Understanding the AWS ML and AI stack
    Introducing Intelligent Document Processing pipeline
        Data capture
        Document classification
        Document extraction
        Document enrichment
        Document post-processing (review and verification)
        Consumption
    Summary
    References
Chapter 2: Document Capture and Categorization
    Technical requirements
        Signing up for an AWS account
    Understanding data capture with Amazon S3
        Data store
        Data sources
        Sensitive document processing
    Understanding document classification with the Amazon Comprehend custom classifier
        Training a Comprehend custom classification model
    Understanding document categorization with computer vision
    Summary
Chapter 3: Accurate Document Extraction with Amazon Textract
    Technical requirements
    Understanding the challenges in legacy document extraction
    Using Amazon Textract for the accurate extraction of different types of documents
        Introducing Amazon Textract
    Using Amazon Textract for the accurate extraction of specialized documents
        Accurate extraction of ID document (driver’s license)
        ID document (US passport) accurate extraction
        Receipt document accurate extraction
        Invoice document accurate extraction
    Summary
Chapter 4: Accurate Extraction with Amazon Comprehend
    Technical requirements
    Using Amazon Comprehend for accurate data extraction
    Understanding document extraction – the IDP extraction stage with Amazon Comprehend
    Understanding custom entities extraction with Amazon Comprehend
        Training an Amazon Comprehend custom entity recognizer
        Checking the performance of a trained model
        Inference result from the Amazon Comprehend custom entity recognizer
    Summary
Part 2: Enrichment of Data and Post-Processing of Data
Chapter 5: Document Enrichment in Intelligent Document Processing
    Technical requirements
    Understanding document enrichment
    Learning to use Amazon Comprehend Medical for accurate extraction of medical entities
        Amazon Comprehend Medical
    Learning to use Amazon Comprehend Medical for medical ontology
    Summary
Chapter 6: Review and Verification of Intelligent Document Processing
    Technical requirements
    Learning post-processing for a completeness check
        Post-processing sensitive data
        Learning about the document review process with human-in-the-loop
    Summary
    References
Chapter 7: Accurate Extraction, and Health Insights with Amazon HealthLake
    Technical requirements
    Introducing Fast Healthcare Interoperability Resources (FHIR)
    Using Amazon HealthLake as a health data store
        FHIR operations with Amazon HealthLake
        READ operation
        HealthLake PUT request
    Handling documents with an FHIR data store
    Summary
    References
Part 3: Intelligent Document Processing in Industry Use Cases
Chapter 8: IDP Healthcare Industry Use Cases
    Technical requirements
    Understanding IDP with healthcare prior authorization
        An introduction to the healthcare prior authorization process
        Automate prior authorization form filling using Amazon HealthLake
    Learning IDP for pharmacy receipt automation
    Understanding healthcare claims processing and risk adjustment with IDP
    Summary
Chapter 9: Intelligent Document Processing – Insurance Industry
    Technical requirements
    Automating the benefits enrollment process with IDP
    Understanding insurance claims processing extraction with IDP
        The data capture and document classification stages of the IDP pipeline
        Document extraction stage of the IDP pipeline
    Understanding insurance claims processing document enrichment and review and verification
        Claims processing for an invalid claims form
    Summary
Chapter 10: Intelligent Document Processing – Mortgage Processing
    Technical requirements
    Automating mortgage processing data capture and data categorization with IDP
    Automating mortgage processing data capture and data categorization with IDP
    Understanding mortgage processing extraction and enrichment with IDP
        Extraction with Comprehend
        Document enrichment for mortgage application processing
        Understanding the mortgage processing review and verification stage of the IDP pipeline
        Understanding financial services use cases for document processing
    Summary
    References:
Index
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