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Getting Started
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
	Install and Setup
	2.90 Reference Documentation

Featured Articles

Did you know?

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 - Using the Labeled Value Extractor Type
Tabular data extraction - Using a Data Table object's Tabular Layout Extract Method
Sectional data extraction - Using a Data Section object's Transaction Detection Extract Method

FYI: The Labeling Behavior and its functionality discussed in this article are often referred to as "Label Set Behavior" or simply "Label Sets".

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.

Feedback

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Other Resources
Grooper x Change Grooper Downloads - Including Grooper installer files Grooper.com	2.80 Reference Documentation 2.80 SDK Documentation 2.90 Reference Documentation	Webinars and Video BIS White Papers Case Studies BIS Blog	ACE Training Schedule First Tuesday User Group Signup

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-[[file:data-rule-icon.png|right]]
 <blockquote style="font-size:14pt">
-'''[[Data Rule]]'''
+''[[Labeling Behavior]]''
 </blockquote>
-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.
+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.
-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:
+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:
-* ''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'''.
+* Document classification - Using the ''Labelset-Based'' '''''Classification Method'''''
-* ''Clear Item'' - This action clears the value of a '''Data Element'''.
+* Field based data extraction - Using the ''Labeled Value'' '''''Extractor Type'''''
-* ''Copy Item'' - This action copies or moves the value of a '''Data Element'''.
+* Tabular data extraction - Using a '''Data Table''' object's ''Tabular Layout'' '''''Extract Method'''''
-* ''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.
+* Sectional data extraction - Using a '''Data Section''' object's ''Transaction Detection'' '''''Extract Method'''''
-* ''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.
+FYI: The ''Labeling Behavior'' and its functionality discussed in this article are often referred to as "Label Set Behavior" or simply "Label Sets".
 |
 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.