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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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|[[Install and Setup]]
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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:separation_and_review_14.png|right|500px|link=Separation and Separation Review|This is an example of the '''Separation Review''' '''Attended Client''' interface.]]
[[File:Table-extraction-simple-table.png|thumb|300px|Data in an Excel spreadsheet is an example of tabular data.]]
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'''[[Separation and Separation Review]]'''
[[Table Extraction]]
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'''Grooper''' uses various approaches and '''[https://en.wikipedia.org/wiki/Algorithm algorithms]''' to determine the classification of a page or folder. The settings on a '''[[Content Model]]''' and '''[[Document Type]]''' add to the complexities for separating pages into documents. Grooper Version 2.9 builds on the '''''Separation''''' settings found on '''Document Types''', including the ability to adjust the '''''Training Scope''''' and configure a '''''Secondary Page Extractor'''''.
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]]'''.


Adjusting the '''Training Scope''' provides benefits to the accuracy and performance of '''ESP Auto Separation''' by focusing what is important when it comes time to separate and classify ''Unstructured'' paginated documents.  For example, the ''Normal'' mode will create a single '''FormType''' and divide trained examples into "First", "Middle" and "Last" pages.  From individual document to individual document, often the most meaningful features composing them are found on the first and last pages, and there can be more variance on the pages in between. This is different from the previous approach, which created individual '''FormTypes''' for each trained example, each with their own "Page X of X" '''PageType''' objects.  This unifies all trained examples into a single '''FormType''', making the training and classification of these documents ultimately simpler and more efficient.  The ''FirstLast'' mode assumes meaningful features for classification are ''only'' found on the first and last pages, with the middle pages containing no information needed to make a separation or classification decision.  With this mode enabled, ''only'' trained examples of the first and last page and their associated features will be saved.  This can improve processing time by removing all the features in the middle pages for consideration.  The ''FirstOnly'' mode narrows this scope even further by only storing features from the first page of trained documents.
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.


Furthermore, ESP Auto Separation ''removes'' but does not ''eliminate'' a lot of the manual work to separate and classify documents. Separation Review is a new review module designed to make the manual work quick and easy.
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.
 
For more information on Separation and Separation Review, visit the full article [[Separation and Separation Review|here]]
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The '''Separation Review''' module was added to improve review of complicated document sets separated and classified by '''ESP Auto Separation'''.  Because this Separation Provider separates using page based classification, it can be important to how it made the decision to separate or not separate a document on a page by page basis.  The '''Classify Review''' module presents the reviewer with pages already placed in document folders, and it can be cumbersome to review the page by page separation viewing documents already placed in folders.
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.


Enter '''Separation Review'''.  This review module is modeled off our '''ESP Auto Separation Tester''' which Grooper Architects use to test the separation and classification of documents, using '''ESP Separation'''.  This viewer gives you a much broader look at the individual pages, allowing an easier (and ultimately quicker and more efficient) view of the separation logic applied to the batch.
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.


There are further quality of life improvements for the '''Separation Review''' module, making the process of reviewing documents separated and classified by '''ESP Auto Separation''' simpler, faster, and more satisfying.
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


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