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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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[[File:Table-extraction-simple-table.png|thumb|300px|Data in an Excel spreadsheet is an example of tabular data.]]
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[[ESP Auto Separation]]
[[Table Extraction]]
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''ESP Auto Separation'' is one of Grooper's '''''[[Separation Provider]]s''''' used for document [[Separation|separation]].  It leverages several different aspects of documents to determine where one document starts and the next begins in a '''Batch''' of loose pages, including classification data, the documents pagination structure, extracted page numbers, and rules for merging one '''Document Type''' with another.  ''ESP Auto Separation'' is also one of the few '''Separation Providers''' that both separates ''and'' classifies documents at the same time, during the '''Separate''' activity.
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]]'''.


''ESP Auto Separation'' (often referred to simply as ''ESP'') is often seen as the most effort intensive '''Separation Provider'''It is a highly configurable provider (And, not all that configuration is done on the '''Separate''' step or a '''Separation Profile'''.  Most of its functionality is actually determined by the associated '''Content Model's''' configuration).  However, it is often ''the'' solution for the most complicated separation and classification challenges.  ''ESP'' is extremely useful for document sets with a variety of structured, semi-structured, and unstructured 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 challengesA 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 (Table Extract Method)|Row Match]]'', ''[[Header-Value (Table Extract Method)|Header-Value]]'', or ''[[Infer Grid (Table Extract Method)|Infer Grid]]'' table extraction methods.
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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 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.

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.

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