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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
Grooper x Change Grooper Downloads - Including Grooper installer files Grooper.com	2.80 Reference Documentation 2.80 SDK Documentation 2.90 Reference Documentation	Webinars and Video BIS White Papers Case Studies BIS Blog	ACE Training Schedule

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+[[File:Table-extraction-simple-table.png|thumb|300px|Data in an Excel spreadsheet is an example of tabular data.]]
 <blockquote style="font-size:14pt">
-''[[Image Processing]]''
+[[Table Extraction]]
 </blockquote>
-Regardless of how good an [[OCR Engine]] is, [[OCR]] is very rarely perfect.  Characters can be segmented out from words wrong.  Artifacts such as table lines, check boxes or even just specks from image noise can interfere with character segmenting and character recognition.  Even when they are segmented out correctly, the OCR engine's character recognition can make the wrong decision about what the character is.
-Image Processing (often abbreviated as "IP") can assist the OCR operation by providing a "cleaner" image to the OCR Engine.  Grooper's robust suite of image processing operations gives you highly configurable control of how your documents are cleaned up before OCR.  Page images may be permanently altered via the '''Image Processing''' activity or temporarily during the '''Recognize''' activity, reverting back to the original image after OCR results are obtained.
+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]]'''.
-These operations generally fall into three categories:
+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.
-# '''Archival Adjustments''' - These are permanent adjustments to the exported document's image.
+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.
-#* Permanent image adjustments are performed when an '''[[IP Profile]]''' is executed during the '''[[Image Processing (Activity)|Image Processing]]''' activity.
-# '''OCR Cleanup''' - Image cleanup can dramatically improve [[OCR]] results.
-#* However, they can also drastically alter the document's image.  Image adjustments are temporarily applied to a document prior to OCR when an '''IP Profile''' is executed during the '''[[Recognize]]''' activity. This is useful for non-destructive image clean up to improve OCR results, keeping the document's pages as their original image to preserve their archival images upon export.
-# '''Layout Data Collection''' - This includes visual information used for data extraction purposes (such as table line locations, barcode information, OMR checkbox states) as well as image features used for [[Visual (Classification Method)|Visual]] classification.
-#* [[Layout Data]] can be collected either during the '''Image Processing''' or the '''Recognize''' activities.
 |
 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.