Intelligent document processing (IDP) is a common use case for customers on AWS. You can utilize Amazon Comprehend and Amazon Textract for a variety of use cases ranging from document extraction, data classification, and entity extraction. One specific industry that uses IDP is insurance. They use IDP to automate data extraction for common use cases such as claims intake, policy servicing, quoting, payments, and next best actions. However, in some cases, an office receives a document with complex, label-less information. This is normally difficult for optical character recognition (OCR) software to capture, and identifying relationships and key entities becomes a challenge. The solution is often requires manual human entry to ensure high accuracy.
In this post, we demonstrate how you can use named entity recognition (NER) for documents in their native formats in Amazon Comprehend to address these challenges.
Solution overview
In an insurance scenario, an insurer might receive a demand letter from an

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