![]() Similar to the insurance industry use case, the payment industry also processes large volumes of semi-structured documents for cross-border payment agreements, invoices, and forex statements. Insurance claim representatives and adjudicators typically spend hundreds of hours manually sifting, sorting, and extracting information from hundreds or even thousands of claim filings. The volume of documents to process and adjudicate an insurance claim can run up to hundreds and even thousands of pages depending on the type of claim and business processes involved. When an insurance claim is filed, it includes documents like insurance claim form, incident reports, identity documents, and third-party claim documents. ![]() A typical insurance claim process involves a claim package that may contain multiple documents. Let’s start by exploring a common use case in the insurance industry. In this post, we discuss a high-level IDP workflow solution design, a few industry use cases, the new features of Amazon Comprehend, and how to use them. This feature simplifies document processing workflows by eliminating any preprocessing steps required to extract plain text from documents, and reduces the overall time required to process them. The following figure compares the previous process to the new procedure and support. With this new release, Amazon Comprehend custom classification and custom entity recognition (NER) supports documents in formats such as PDF, TIFF, PNG, and JPEG directly, without the need to extract UTF8 encoded plain text from them. Support for documents in native formats for custom entity recognition real-time analysis.Support for documents in native formats for custom classification real-time analysis and asynchronous jobs.Specifically, we are announcing the following capabilities: Today, we are excited to announce one-step document classification and real-time analysis for NER for semi-structured documents in native formats (PDF, TIFF, JPG, PNG) using Amazon Comprehend. Last year, we announced support for native document formats with custom named entity recognition (NER) asynchronous jobs. This two-step process introduces complexities in document processing workflows. ![]() Similarly, for custom entity recognition in real time, preprocessing to extract text is required for semi-structured documents such as PDF and image files. Today, Amazon Comprehend supports classification for plain text documents, which requires you to preprocess documents in semi-structured formats (scanned, digital PDF or images such as PNG, JPG, TIFF) and then use the plain text output to run inference with your custom classification model. Customers spend a significant amount of time and effort identifying documents and extracting critical information from them for various use cases. The goal of Amazon’s intelligent document processing (IDP) is to automate the processing of large amounts of documents using machine learning (ML) in order to increase productivity, reduce costs associated with human labor, and provide a seamless user experience. ![]() IDP uses natural language technologies and computer vision to extract data from structured and unstructured content, especially from documents, to support automation and augmentation.” – Gartner “Intelligent document processing (IDP) solutions extract data to support automation of high-volume, repetitive document processing tasks and for analysis and insight. ![]()
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