The following steps deploy the reference implementation in your AWS account. AWS Command Line Interface (AWS CLI)-for instructions, see Installing the AWS CLI).Prerequisitesīefore you get started, you must install the following prerequisites: When the human review is complete, the callback task token is used to resume the state machine, combine the pages’ results, and store them in an output Amazon Simple Storage Service (Amazon S3) bucket.įor more information about the demo solution, see the GitHub repo. If triggered, Amazon Textract returns the extracted text and data along with the details. This workflow is configured to trigger when form fields are detected below a certain confidence threshold. When we call Amazon Textract, we also specify the Amazon A2I workflow as part of the request. It then uses the Map state to process multiple pages concurrently using the AnalyzeDocument API. As the workflow starts, it extracts individual pages from the multi-page PDF document. To implement this architecture, we take advantage of Amazon Step Functions to build the overall workflow. Although Amazon Textract can process images (PNG and JPG) and PDF documents, Amazon A2I human reviewers need to have individual pages as images and process them individually using the AnalyzeDocument API of Amazon Textract. The following architecture shows how you can have a serverless architecture to process multi-page PDF documents with a human review. In this post, we show how you can use Amazon Textract and Amazon A2I to build a workflow that enables multi-page PDF document processing with a human reviewers loop. For more information, see Using with Amazon Textract with Amazon Augmented AI for processing critical documents. This allows human review of ML predictions when needed based on a confidence score threshold, and you can audit the predictions on an ongoing basis. Amazon Augmented AI (Amazon A2I) allows you to build and manage such human review workflows. For example, extracting information from a scanned mortgage application or medical claim form might require human review of certain fields due to regulatory requirements or potentially low-quality scans. For example, it can extract patient information from an insurance claim or values from a table in a scanned medical chart.ĭepending on the business use case, you may want to have a human review of ML predictions. Amazon Textract is a machine learning (ML) service that makes it easy to process documents at a large scale by automatically extracting text and data from virtually any type of document. Healthcare and life science organizations, for example, need to access data within medical records and forms to fulfill medical claims and streamline administrative processes. Businesses across many industries, including financial, medical, legal, and real estate, process a large number of documents for different business operations.
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