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It provides a workflow and a platform for your own or a partner workforce (humans referred to as labelers in hitl) to review, validate, and correct the data extracted from documents by document ai processors. This guide shows you how to implement hitl within crewai. Hitl allows engineers to incorporate human review and correction steps into ai document processing pipelines, ensuring higher data accuracy through validation and manual overrides
New customers are not allowlisted In this codelab, you will learn how to use document ai human in the loop to complete human review tasks with specialized processors. If you want to use (hitl) but don't see the option available, contact your google account team
To implement a human review and correction solution that meets your requirements, we recommend working with a google cloud certified partner.
I'm using robotic process automation (rpa) combined with azure document intelligence (adi) to automate document processing I've built and trained a custom model in adi using document intelligence studio My rpa tool submits documents for processing using the adi post api, retrieves the extracted data using the get api, and displays it on a user interface for verification and accuracy The user interface offers you the opportunity to continuously improve the ai through your corrections
This enables you to continuously improve your individual process portfolio. Successful hitl implementation requires clear review points, intuitive ui design, defined exception rules, and measurable kpis Common pitfalls include unclear human roles, poor review interfaces, poor performance tracking, and compliance oversights This optional step is crucial when 100% accuracy is required or when a document doesn’t meet the specific validation rules established for each ai model
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