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Intelligent Document Processing with AWS AI/ML

You're reading from   Intelligent Document Processing with AWS AI/ML A comprehensive guide to building IDP pipelines with applications across industries

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Product type Paperback
Published in Oct 2022
Publisher Packt
ISBN-13 9781801810562
Length 246 pages
Edition 1st Edition
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Author (1):
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Sonali Sahu Sonali Sahu
Author Profile Icon Sonali Sahu
Sonali Sahu
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Table of Contents (16) Chapters Close

Preface 1. Part 1: Accurate Extraction of Documents and Categorization
2. Chapter 1: Intelligent Document Processing with AWS AI and ML FREE CHAPTER 3. Chapter 2: Document Capture and Categorization 4. Chapter 3: Accurate Document Extraction with Amazon Textract 5. Chapter 4: Accurate Extraction with Amazon Comprehend 6. Part 2: Enrichment of Data and Post-Processing of Data
7. Chapter 5: Document Enrichment in Intelligent Document Processing 8. Chapter 6: Review and Verification of Intelligent Document Processing 9. Chapter 7: Accurate Extraction, and Health Insights with Amazon HealthLake 10. Part 3: Intelligent Document Processing in Industry Use Cases
11. Chapter 8: IDP Healthcare Industry Use Cases 12. Chapter 9: Intelligent Document Processing – Insurance Industry 13. Chapter 10: Intelligent Document Processing – Mortgage Processing 14. Index 15. Other Books You May Enjoy

Summary

In this chapter, we discussed the core features of Amazon Comprehend, PII detection and redaction, and Amazon Comprehend Medical’s PHI detection feature. We also discussed the Review and Validation stage of the IDP pipeline and why it is critical for accurate IDP. We also discussed how to leverage Amazon Textract to extract text from any type of document and then pass it to Amazon Comprehend (Medical) for PII or PHI information detection and redaction. This helps to build a document processing pipeline to handle sensitive information.

We then reviewed the need for human review. We also discussed Amazon A2I and its core features for including human beings in the review of more critical field elements in documents, or ones with lower accuracy. This automation helps build cost-effective document processing with time acceleration.

In the next chapter, we will discuss how to build a data lake for health information and how IDP can be integrated with Amazon HealthLake...

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