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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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''[[Labeled OMR]]''
'''[[Labeling Behavior|Label Sets]]'''
</blockquote>
</blockquote>
[[File:Labeled-omr-about-01.png|thumb|200px|An example of checkboxes.]]


''Labeled OMR'' is an extractor used to output OMR checkbox labels.  It determines whether labeled checkboxes are checked or not and, if checked, outputs the label as its result.
"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.


Documents use checkboxes to make our life easier.  They are particularly prevalent on structured forms.  It gives the person filling out the form the ability to just check a box next to a series of options rather than typing in the information.


However, most of Grooper's extraction centers around regular expression, matching text patterns and returning the result.  There isn't necessarily a character to match a checked checkbox.  Regular expression isn't going to cut it to determine if a box is checked or not.   
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:


This is where OMR comes into play.  OMR stands for "Optical Mark Recognition".  OMR determines checkbox states.  The basic idea behind it is very simple.  First find a box.  A box is just four lines connected to each other in a square-like fashion.  If that box has a mark of some kind inside it, it is checked.  If not, it's not.  Checked (or marked) boxes, whether a checked "x" (<span style="font-size:120%">&#9746;</span>), a checkmark (<span style="font-size:120%">&#9745;</span>),  or a check block (<span style="font-size:120%">&#9635;</span>), while have more black pixels inside the box than an unchecked (or unmarked) one (<span style="font-size:120%">&#9744;</span>).  If the detected box has a high threshold of black pixels in it, it's checked (or marked).  If not, it's unchecked (or unmarked). 
* 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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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 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 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.
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.
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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]]'''
|-
|[[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)]]
|-
|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)]]'''
|-
|colspan="4"|[[Image:Linq_to_grooper_objects_001.png|center|200px|link=LINQ to Grooper Objects]]
|-
|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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:: <div id="gartner"></div>
[[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 [https://www.gartner.com/reviews/market/data-integration-tools/vendor/bis?utm_source=bis&utm_medium=referral&utm_campaign=widget&utm_content=ZWYwMDE5NDAtZmNiYi00OTYyLThjY2QtNzM0MzcwMDA5NzA3 Gartner.com].<br><br>Click "Submit a review" on the image to the left to start 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%"|
|style="width:25%"|
* [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


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