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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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Data Rule The Data Rule object allows for complex validation and manipulation of a Data Model's Data Elements (Data Fields, Data Sections, and Data Tables) in Grooper. This allows users to create a conditional hierarchy of actions to take if certain conditions met. These conditions are configured using .NET, LINQ and/or lambda expressions. When the expression is "triggered", either evaluating to "true" or "false", certain actions can be made. These include: Calculate Value - This action sets the value of a Data Field or cells a Data Column, using calculate expressions to perform mathematical or concatenation operations of Data Elements. Clear Item - This action clears the value of a Data Element. Copy Item - This action copies or moves the value of a Data Element. Parse Value - This action uses a regular expression pattern to return part of a Data Field's value or cell in a Data Column's value. Raise Issue - This action adds an issue to the issue log, used for validating a Data Element. This action can also be used to flag the Data Element. These trigger conditions and subsequent actions set on the Data Rules objects are executed through the Apply Rules activity after data is extracted from an Extract activity.

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 2.9

Featured Use Case

Welcome to Grooper 2.9! Below you will find helpful links to all the articles about the new/changed functionality in this version of Grooper. Compile Stats Microsoft Office Integration Document Viewer Separation and Separation Review Data Review Confidence Multiplier Data Element Overrides Database Export CMIS Lookup Content Type Filter Output Extractor Key Box (CMIS Binding) LINQ to Grooper Objects

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.

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