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Advances in generative artificial intelligence (AI) have given rise to intelligent document processing (IDP) solutions that can automate the document classification, and create a cost-effective classification layer capable of handling diverse, unstructured enterprise documents. Categorizing documents is an important first step in IDP systems.
Document processing has witnessed significant advancements with the advent of Intelligent Document Processing (IDP). With IDP, businesses can transform unstructured data from various document types into structured, actionable insights, dramatically enhancing efficiency and reducing manual efforts.
With AWS intelligent document processing (IDP) using AI services such as Amazon Textract , you can take advantage of industry-leading machine learning (ML) technology to quickly and accurately process data from PDFs or document images (TIFF, JPEG, PNG). As mentioned earlier, there is both a notebook version and a Python script version.
Solution overview To solve this problem, you can identify one or more unique metadata information that is associated with the documents being indexed and searched. In Amazon Kendra, you provide document metadata attributes using custom attributes. When the authentication is performed using Amazon Cognito, the “sessionState”.”sessionAttributes”.”idtokenjwt”
The postprocessing component uses bounding box metadata from Amazon Textract for intelligent data extraction. The Apache Tika open-source Python library is used for data extraction from word documents. Amazon DynamoDB is used for storing document metadata and keeping track of the document processing status across all key components.
This allows you to directly manage user access to Amazon Q Business applications from your enterprise identity provider (IdP), such as Okta or PingFederate. This involves a setup described in the following steps: Create a SAML or OIDC application integration in your IdP account.
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