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Grooper was built from the ground up by BIS, a company with 35 years of continuous experience developing and delivering new technology. Grooper is an intelligent document processing and digital data integration solution that empowers organizations to extract meaningful information from paper/electronic documents and other forms of unstructured data. The platform combines patented and sophisticated image processing, capture technology, machine learning, natural language processing, and optical character recognition to enrich and embed human comprehension into data. By tackling tough challenges that other systems cannot resolve, Grooper has become the foundation for many industry-first solutions in healthcare, financial services, oil and gas, education, and government.	Getting Started
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	2021 Reference and SDK Documentation

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Label Sets "Label Sets" refers to a variety of document classification and extraction capabilities made possible through the Labeling Behavior. The Labeling Behavior is a Content Type Behavior designed to collect and utilize a document's field labels in a variety of ways. This includes functionality for classification and data extraction. The Labeling Behavior functionality allows Grooper users to quickly onboard new Document Types for structured and semi-structured forms, utilizing labels as a thumbprint for classification and data extraction purposes. Once the Labeling Behavior is enabled, labels are identified and collected using the "Labels" tab of Document Types. These "Label Sets" can then be used for the following purposes: Document classification - Using the Labelset-Based Classification Method Field based data extraction - Primarily using the Labeled Value Extractor Type Tabular data extraction - Primarily using a Data Table object's Tabular Layout Extract Method Sectional data extraction - Primarily using a Data Section object's Transaction Detection Extract Method

The earliest examples of OCR (Optical Character Recognition) can be traced back to the 1870s. Early OCR devices were actually invented to aid the blind. This included "text-to-speech" devices that would scan black print and produce sounds a blind person could interpret, as well as "text-to-tactile" machines which would convert luminous sensations into tactile sensations. Machines such as these would allow a blind person to read printed text not yet converted to Braille. The first business to install an OCR reader was the magazine Reader's Digest in 1954. The company used it to convert typewritten sales reports into machine readable punch cards. It would not be until 1974 that OCR starts to form as we imagine it now with Ray Kurzweil's development of the first "omni-font" OCR software, capable of reading text of virtually any font.

New in Version 2021

Featured Use Case

Welcome to Grooper 2021! Welcome to Grooper 2021! Grooper version 2021 is here! There's a slew of new features, "under-the-hood" architecture improvements, and simplified redesigns to make this version both easiest to use and provide the most accurate capture capabilities to date. New feature improvements include: Behaviors This new set of features centralizes the Content Model as the logical hub of document processing, allowing for new functionality and expanding and simplifying set up of existing functionality. Label Sets A new way of document classification and extraction using labels. Smart PDFs New PDF generation functionality (via the PDF Data Mapping Behavior), including embedding extracted data directly to PDF files. Rules Engine The Data Rule is a new object designed for hierarchical conditional validation and calculation of Data Elements in a Data Model. This "Rules Engine" drives complex data validation and calculations never before possible in Grooper. Document Ingestion API Integration of a new RESTful document ingestion API provides the ability to create and populate batches, and the ability to monitor the status of batch processes, and retrieve results. Value Reader A new data extraction object, designed to centralize all of Grooper's extraction functionality into a single object, including its pattern-based, OMR, and zonal types of extraction. For more information on these and other improvements, visit the What's New in Grooper 2021 article.

They’re Saving Over 5,000 Hours Every Year in Data Discovery and Processing American Airlines Credit Union has transformed their data workflows, quickly saving thousands of hours in electronic data discovery , resulting in much greater efficiency and improved member services. Discover how they: Quickly found 40,000 specific files among one billion Easily integrated with data silos and content management systems when no other solution would Have cut their mortgage processing time in half (and they process mortgages for 47 branch offices!) Learn from the document and electronic data discovery experts at BIS! You can access the full case study clicking this link.