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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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|[http://grooper.bisok.com/Documentation/2.80/Main/HTML5/index.htm#t=Start_Page.htm 2.80 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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[[File:weighted_rules_00.png|right|250px|link=Confidence Multiplier]]
[[File:Table-extraction-simple-table.png|thumb|300px|Data in an Excel spreadsheet is an example of tabular data.]]
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'''[[Confidence Multiplier]]'''
[[Table Extraction]]
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The '''''Confidence Multiplier''''' property of a '''[[Data Type]]''' allows you to manually alter the confidence score of returned values.


Use of this property is sometimes referred to as ''weighted rules''.  Its practical application allows a user to arbitrarily set the confidence of a '''Data Type''' extractor's result or increase or decrease the result's confidence.  This changes the confidence of the extractor's results, making them appear more (or less) favorable.  When used in combination with the '''''Order By''''' property set to ''Confidence'' on a parent '''Data Type''', you can manipulate which result the parent prioritizes.
Table Extraction refers to Grooper's functionality to extract data from cells in tablesThis is accomplished by configuring the '''[[Data Table]]''' '''''[[Data Element]]''''' in a '''[[Data Model]]'''.


In effect, you change the ''rules'' about the ''weight'' or priority a particular child extractor by changing its confidence scores.
Tables are one of the most common ways data is organized on documents.  Human beings have been writing information into tables before they started writing literature, even before paper was invented.  There are examples of tables carved onto the walls of Egyptian temples!  They are excellent structures for representing a lot of information with various characteristics in common in a relatively small space (or an Egyptian temple sized space).  However, targeting the data inside them presents its own set of challenges.  A table’s structure can range from simple and straightforward to more complex (even confounding).  Different organizations may organize the same data differently, creating different tables for what, essentially, is the same data.


===General Usage===
In Grooper, tabular data can be extracted to '''Data Table''' objects using the ''[[Row Match (Table Extract Method)|Row Match]]'', ''[[Header-Value (Table Extract Method)|Header-Value]]'', or ''[[Infer Grid (Table Extract Method)|Infer Grid]]'' table extraction methods.
Modifying the '''Confidence Multiplier''' property of a '''Data Type'''  is done by clicking on the ellipses in the '''''Result Options''''' property which opens the '''''Result Options''''' submenu.
 
The '''''Confidence Multiplier''''' property defaults to ''1'' and can be changed in this submenu. The field is a double and takes floating point values. 
 
For example, a value of ''0.5'' will multiply the confidence of output results by 0.5. If the output confidence was 100%, now it will be 50%. Similarly, you can increase the confidence, even above 100%. If the '''''Confidence Multiplier''''' property is set to ''3'', and an output result had a 50% confidence, it will not display as 150% confidence.
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You can talk to us!
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.


Do you have an idea for an article?  Have you noticed something missing from one already in the wiki? Do you have other comments or feedback about the wiki?
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.


If so, check out the [https://xchange.grooper.com/categories/documentation-requests Documentation Requests] section of Grooper x Change.  This is your way to communicate with our documentation team.  Please, let us know how we can continue to improve our wiki.
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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[https://www.bisok.com/case-studies/electronic-data-discovery-case-study/ You can access the full case study clicking this link].
[https://www.bisok.com/case-studies/electronic-data-discovery-case-study/ You can access the full case study clicking this link].
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==== <span style="color:white">Feedback</span> ====
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[[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-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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* [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]
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* [https://blog.bisok.com/webinars Webinars and Video]
* [https://blog.bisok.com/webinars Webinars and Video]

Revision as of 12:19, 22 February 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?
Data in an Excel spreadsheet is an example of tabular data.

Table Extraction

Table Extraction refers to Grooper's functionality to extract data from cells in tables. This is accomplished by configuring the Data Table Data Element in a Data Model.

Tables are one of the most common ways data is organized on documents. Human beings have been writing information into tables before they started writing literature, even before paper was invented. There are examples of tables carved onto the walls of Egyptian temples! They are excellent structures for representing a lot of information with various characteristics in common in a relatively small space (or an Egyptian temple sized space). However, targeting the data inside them presents its own set of challenges. A table’s structure can range from simple and straightforward to more complex (even confounding). Different organizations may organize the same data differently, creating different tables for what, essentially, is the same data.

In Grooper, tabular data can be extracted to Data Table objects using the Row Match, Header-Value, or Infer Grid table extraction methods.

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

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