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

246 Pages·2022·23.948 MB·English

by  Sonali Sahu

Intelligent Document Processing
with AWS AI/ML
A comprehensive guide to building IDP pipelines with
applications across industries
Sonali Sahu
BIRMINGHAM—MUMBAI
Intelligent Document Processing with AWS AI/ML
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For my parents, for always loving and believing in me.
For all the women who can do whatever they want, when they want – or will one day.
Co n t r i b u t o r s
About the authors
Sonali Sahu is a leading Intelligent Document Processing Artificial Intelligence (AI) and Machine
Learning (ML) solutions architect on the team at Amazon Web Services. She is a passionate technophile
and enjoys working with customers to solve complex problems using innovation. Her core area of focus
is AI and ML. She has both breadth and depth of experience working with technology, with industry
expertise in healthcare and insurance. She has significant architecture and management experience
in delivering large-scale programs across various industries and platforms.
About the reviewer
Winnie Tung has over 30 years of experience solving some of the world’s most difficult technical
problems in the financial services industry. She is currently modernizing the AI/ML platform at JPMC.
Before that, she worked in AWS Professional Services, specializing in developing AI/ML solutions for
the real world. She helps customers to operationalize and manage AI/ML solutions at scale.

Table of Contents
Preface xiii
Part 1: Accurate Extraction of Documents and
Categorization
1
Intelligent Document Processing with AWS AI and ML 3
Understanding common Document classification 11
document processing use cases Document extraction 11
across industries 4 Document enrichment 11
Understanding the AWS ML Document post-processing
and AI stack 5 (review and verification) 12
Consumption 12
Introducing Intelligent Document
Processing pipeline 9 Summary 13
Data capture 10 References 13
2
Document Capture and Categorization 15
Technical requirements 15 Data store 20
Signing up for an AWS account 15 Data sources 20
Sensitive document processing 27
Understanding data capture
with Amazon S3 20
viii Table of Contents
Understanding document Understanding document
classification with the Amazon categorization with computer
Comprehend custom classifier 29 vision 38
Training a Comprehend custom Summary 44
classification model 31
3
Accurate Document Extraction with Amazon Textract 45
Technical requirements 45 Using Amazon Textract for the
Understanding the challenges in accurate extraction of specialized
legacy document extraction 45 documents 56
Using Amazon Textract for the Accurate extraction of ID document
(driver’s license) 57
accurate extraction of different
ID document (US passport) accurate
types of documents 47
extraction 58
Introducing Amazon Textract 47
Receipt document accurate extraction 60
Invoice document accurate extraction 62
Summary 65
4
Accurate Extraction with Amazon Comprehend 67
Technical requirements 67 Understanding custom entities
Using Amazon Comprehend for extraction with Amazon
accurate data extraction 67 Comprehend 77
Understanding document Training an Amazon Comprehend
custom entity recognizer 78
extraction – the IDP extraction
Checking the performance of a
stage with Amazon
trained model 81
Comprehend 73
Inference result from the Amazon
Comprehend custom entity
recognizer 82
Summary 84
Table of Contents ix
Part 2: Enrichment of Data and Post-Processing
of Data
5
Document Enrichment in Intelligent Document Processing 89
Technical requirements 89 Amazon Comprehend Medical 93
Understanding document Learning to use Amazon
enrichment 90 Comprehend Medical for
Learning to use Amazon medical ontology 105
Comprehend Medical for accurate Summary 109
extraction of medical entities 93
6
Review and Verification of Intelligent Document Processing 111
Technical requirements 111 Learning about the document review
process with human-in-the-loop 127
Learning post-processing for a
completeness check 112 Summary 130
Post-processing sensitive data 115 References 131
7
Accurate Extraction, and Health Insights with Amazon HealthLake 133
Technical requirements 133 READ operation 140
Introducing Fast Healthcare HealthLake PUT request 142
Interoperability Resources
Handling documents with
(FHIR) 134
an FHIR data store 145
Using Amazon HealthLake
Summary 151
as a health data store 134
References 152
FHIR operations with Amazon
HealthLake 138