PARASCRIPT FORMXTRA CAPABILITIES OVERVIEW WITH DEMO
Aired on Tuesday, Jan 30, 2018 @ 9 AM PT | 10 AM MT | 11 AM CT| 12 PM ET
Join us to discover the latest Parascript FormXtra capabilities and watch a live document classification demo.
Find out about all of these features:
- Advanced Image Processing and Virtual Drop-out. Businesses must process documents that arrive from all points of capture whether smart phone, fax, email or traditional mail. Dealing with the large variance in image quality resulting with differences in capture capabilities makes it almost impossible to achieve high-levels of automation.With FormXtra 6.3, Parascript introduces the most comprehensive image processing capabilities available. FormXtra enables companies to deal with any document, all within the same workflow with levels of performance that previously could only be observed with controlled high-quality scanners. Black and white forms such as claims can be converted to simulate drop-out ink forms; images are analyzed, and those that vary in size are reformatted to conform to expected layouts. Mobile images are transformed into high-quality scans—even without specialized mobile apps.
- Highest performing Claims Classification & Recognition. Parascript has implemented Virtual Drop-out for Claims documents that results in the industry’s best classification and recognition solution for black-and-white claims, regardless of image quality or scale. The new claims module provides near-perfect classification and improves drop-out and black-and-white claims recognition at the highest level in the industry.
- Easy-to-use Document Classification (join us to see the demo!). Document classification automation has just gotten easier and more accurate. FormXtra 6.3 enables documents to be organized and tested leveraging the text-based classifier, image classifier or a combination. While text is most-often used as the primary input in most capture systems, Parascript software can leverage both visual analysis of documents and text to accurately classify documents.
- State-of-the-art field-level accuracy for invoice data. Using new deep-learning technology, invoice data location for header-footer data is now better than 80 percent with 95 percent accuracy at the field level, requiring no external validation data source and no manual validation necessary. Additionally, line item location is now better than 55 percent with 98 percent accuracy with no PO matching requirement.