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Grooper is a software application that helps organizations innovate workflows by integrating difficult data.
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.


Grooper empowers rapid innovation for organizations processing and integrating large quantities of difficult data. Created by a team of courageous developers frustrated by limitations in existing solutions, Grooper is an intelligent document and digital data integration platform. Grooper combines patented and sophisticated image processing, capture technology, machine learning, and natural language processing. Grooper – intelligent document processing; limitless, template-free data integration.
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.


|[https://xchange.grooper.com/discussion/57/read-me-getting-started Getting Started]
|[https://xchange.grooper.com/discussion/57/read-me-getting-started Getting Started]
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|[https://grooper.bisok.com/Documentation/2.90/Main/HTML5/index.htm#t=Start_Page.htm 2.90 Reference Documentation]
|[https://grooper.bisok.com/Documentation/2.90/Main/HTML5/index.htm#t=Start_Page.htm 2.90 Reference Documentation]
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[[Data Context]]
'''[[Labeling Behavior|Label Sets]]'''
</blockquote>
</blockquote>


Data without context is meaningless.  Context is critical to understanding and modeling the relationships between pieces of information on a documentWithout context, it’s impossible to distinguish one data element from another.  Context helps us understand what data refers to or “means”. 
"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 waysThis includes functionality for classification and data extraction.
 
This allows us to build an extraction logic using '''[[Data Type]]''' and '''[[Field Class]]''' extractors in order to build and populate a '''[[Data Model]]'''.   


There are three fundamental data context relationships:


* '''Syntactic''' - Context given by the syntax of data.
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:
* '''Semantic''' - Context given by the lexical content associated with the data.
* '''Spatial''' - Context given by where the data exists on the page, in relationship with other data.


Using the context these relationships provide allows us to understand how to target data with extractors.
* 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'''''
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You can now manually manipulate the confidence of an extraction resultThe '''''[[Confidence Multiplier and Output Confidence]]''''' properties of '''[[Data Type]]''' and '''[[Data Format]]''' extractors allow you to change the confidence score of extraction resultsNo longer are you forced to accept the score Grooper providesThese properties give you more control when it comes to what confidence a result ''should'' be.
The earliest examples of OCR (Optical Character Recognition) can be traced back to the 1870sEarly OCR devices were actually invented to aid the blindThis 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 sensationsMachines such as these would allow a blind person to read printed text not yet converted to Braille.


This allows you to prioritize certain results over others.  You can create a kind of "fall back" or "safety net" result by using this property.  You can even ''increase'' the confidence of an extractor's result, allowing you to give more weight to a fuzzy extractor's result over a non-fuzzy one, for example.
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.


For more information visit, the [[Confidence Multiplier and Output Confidence]] article.
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.
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|style="width:50%"|'''New in 2.9'''||'''Featured Use Case'''
|style="width:50%"|'''New in Version 2021'''||'''Featured Use Case'''
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== Welcome to Grooper 2021! ==
!colspan="4" style="padding: 25px" | <blockquote style="font-size:14pt">Welcome to '''Grooper 2.9'''!<br/>Below you will find helpful links to all the articles about the new/changed functionality in this version of '''Grooper'''.</blockquote>
 
|-
[[File:Grooper-2021-round.png|thumb|200px|link=https://wiki.grooper.com/index.php?title=What%27s_New_in_Grooper_2021|Welcome to Grooper 2021!]]
|[[Image:Compile_stats_02.png|center|200px|link=Compile Stats]]
 
|[[Image:Microsoft_office_integration_000.png|center|200px|link=Microsoft Office Integration]]
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.
|[[Image:document_viewer_00.png|center|150px|link=Document Viewer]]
 
|[[Image:Separation_and_review_18.png|center|175px|link=Separation and Separation Review]]
New feature improvements include:
|-
 
|style="text-align:center"|'''[[Compile Stats]]'''
* [[Behaviors]]
|style="text-align:center"|'''[[Microsoft Office Integration]]'''
** 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.
|style="text-align:center"|'''[[Document Viewer]]'''
* [[Labeling Behavior|Label Sets]]
|style="text-align:center"|'''[[Separation and Separation Review]]'''
** A new way of document classification and extraction using labels.
|-
* [[PDF Data Mapping|Smart PDFs]]
|[[Image:data_review_00.png|center|200px|link=Data Review]]
** New PDF generation functionality (via the ''PDF Data Mapping'' '''''Behavior'''''), including embedding extracted data directly to PDF files.
|[[Image:Weighted_rules_00.png|center|200px|link=Confidence Multiplier]]
* [[Data Rule|Data Engine]]
|[[Image:Data_element_overrides_000.png|center|150px|link=Data Element Overrides]]
** The '''Data Rule''' is a new object designed for hierarchical conditional validation and calculation of '''Data Elements''' in a '''Data Model'''.  This "Data Engine" drives complex data validation and calculations never before possible in Grooper.
|[[Image:Database_export_002.png|center|200px|link=Database Export]]
* [[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.
|style="text-align:center"|'''[[Data Review]]'''
* Document Ingestion API
|style="text-align:center"|'''[[Confidence Multiplier]]'''
** 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.
|style="text-align:center"|'''[[Data Element Overrides]]'''
|style="text-align:center"|'''[[Database Export]]'''
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|[[Image:Cmis_lookup_002.png|center|200px|link=CMIS Lookup]]
|[[Image:Content_type_filter_000.png|center|100px|link=Content Type Filter]]
|[[Image:Output_extractor_key_000.png|center|200px|link=Output Extractor Key]]
|[[Image:box_cmis_binding_000.png|center|200px|link=Box (CMIS Binding)]]
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|style="text-align:center"|'''[[CMIS Lookup]]'''
|style="text-align:center"|'''[[Content Type Filter]]'''
|style="text-align:center"|'''[[Output Extractor Key]]'''
|style="text-align:center"|'''[[Box (CMIS Binding)]]'''
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|colspan="4"|[[Image:Linq_to_grooper_objects_001.png|center|200px|link=LINQ to Grooper Objects]]
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|colspan="4" style="text-align:center"|'''[[LINQ to Grooper Objects]]'''
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For more information on these and other improvements, visit the [[What's New in Grooper 2021]] article.
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[[File:American-airlines-credit-union-financial-services-document-data-capture-integration-grooper.jpg|400px|right|link=https://www.bisok.com/case-studies/electronic-data-discovery-case-study/]]
[[File:American-airlines-credit-union-financial-services-document-data-capture-integration-grooper.jpg|400px|right|link=https://www.bisok.com/case-studies/electronic-data-discovery-case-study/]]
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:: <span id="gartner"></span>
[[File:Gartner pi.jpg|link=https://www.gartner.com/reviews/market/data-and-analytics-others/vendor/bis/product/grooper]]
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<span style="font-size:14pt">We value your feedback!<br><br>Help us improve our product by leaving us a review on gartner.com.<br><br>Click the image to the left to leave us a review.</span>
<span style="font-size:14pt">We value your feedback!<br><br>Help us improve our product by leaving us a review on [https://www.gartner.com/reviews/market/data-and-analytics-others/vendor/bis/product/grooper Gartner.com].<br><br>Click the image to the left to submit a review.</span>
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* [https://www.bisok.com/grooper-data-integration-platform/ Grooper.com]
* [https://www.bisok.com/grooper-data-integration-platform/ Grooper.com]
|style="width:25%"|
|style="width:25%"|
* [https://grooper.bisok.com/Documentation/21.0/index.htm#t=Start_Page.htm 2021 Reference and SDK Documentation]
* [https://grooper.bisok.com/Documentation/2.90/Main/HTML5/index.htm#t=Start_Page.htm 2.90 Reference Documentation]
* [http://grooper.bisok.com/Documentation/2.80/Main/HTML5/index.htm#t=Start_Page.htm 2.80 Reference Documentation]
* [http://grooper.bisok.com/Documentation/2.80/Main/HTML5/index.htm#t=Start_Page.htm 2.80 Reference Documentation]
* [http://grooper.bisok.com/Documentation/2.80/SDK/HTML5/index.htm#t=Developer_Reference.htm 2.80 SDK Documentation]
* [http://grooper.bisok.com/Documentation/2.80/SDK/HTML5/index.htm#t=Developer_Reference.htm 2.80 SDK Documentation]
* [https://grooper.bisok.com/Documentation/2.90/Main/HTML5/index.htm#t=Start_Page.htm 2.90 Reference Documentation]
|style="width:25%"|
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* [https://blog.bisok.com/webinars Webinars and Video]
* [https://blog.bisok.com/webinars Webinars and Video]
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* [[ACE Training Schedule]]
* [[ACE Training Schedule]]
* [https://go.bisok.com/first-tuesday-grooper-technical-user-group First Tuesday User Group Signup]
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Revision as of 09:13, 20 September 2021

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?

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.
  • Data Engine
    • The Data Rule is a new object designed for hierarchical conditional validation and calculation of Data Elements in a Data Model. This "Data Engine" drives complex data validation and calculations never before possible in Grooper.
  • 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.
  • 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.

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.

Feedback

Feedback

We value your feedback!

Help us improve our product by leaving us a review on Gartner.com.

Click the image to the left to submit a review.


Other Resources