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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 how to build a data capture stage for the IDP pipeline. Data is your gold mine, and you need a secure, scalable, and reliable data store. We introduced Amazon S3 and how you can leverage an object store to aggregate and store data in a scalable and highly available manner. We also described the data capture stage, with documents of varying layouts, formats, and types.

We then reviewed the need for document classification and categorization, with examples including mortgage processing and insurance claims processing. We discussed Amazon Comprehend and its custom classification feature. This chapter also gave you a hands-on experience in how to classify documents as invoice and receipt types. Moreover, we also looked at Amazon Rekognition, and how we can use its Custom Label feature to classify documents on its structural formats. You also had hands-on experience in classifying documents with the presence of the AWS logo or a non-AWS logo.

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