Streamlining government services with intelligent document processing

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As federal agency leaders look to modernize operations and streamline manual workflow processes with automation, one of the areas that creates significant bottlenecks is document and data processing.

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Public sector organizations must efficiently and effectively process millions of forms, applications and images — some digital, some handwritten — to meet the needs of mission-critical workflows. And document formats are continuously changing and vary in quality and complexity, making it difficult to reliably and efficiently process and extract data at scale.

“Intelligent document processing (IDP) is a powerful example of intelligent automation in action, providing tangible value for organizations that are looking to increase efficiency and accuracy when dealing with huge volumes of data,” according to a recent report from Hyperscience.

By leveraging advanced artificial intelligence (AI) and machine learning (ML) techniques, Hyperscience’s proprietary extraction engine is able to classify and extract data across all types of documents — including handwritten forms, PDFs and low-quality images — with higher accuracy and automation than existing solutions.

“Hyperscience delivers up to 95% of data entry with over 99% accuracy, far surpassing the average industry accuracy rate, which hovers around 55%,” shares the report. “Out-of-the-box, clients can expect around 80% automation,” which continues to get better as the ML solution learns and trains on the data it’s exposed to, which increases throughput without sacrificing accuracy.

Learn more about how Hyperscience’s automation platform is helping government agencies deliver increased efficiencies and better agency outcomes.

This article was produced by FedScoop for, and sponsored by, Hyperscience.

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artificial intelligence (AI), automation, Content / Document Managment, Content Syndication, Digital Transformation, Hyperscience, Hyperscience 2021, IT Modernization, machine learning, Sponsored Content
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