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Revision as of 11:46, 15 December 2020

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?

Separation

Separation, in Grooper, is the process of turning loose pages into documents, by determining points in a Batch at which Batch Folders are created and subsequent Batch Pages are placed inside.

Pages are organized into document folders during the Separate activity. There are a variety of methods to separate pages into documents during this activity, including (but not limited to) the use of printed control sheets, defined page lengths, and extractible text content. The specific separation method is determined by the Separation Provider and its configuration used during the Separate activity. You may also save and re-use a Separation Provider's configuration settings by creating a Separation Profile.

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:Separation-about.png|right|350px]]
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-''[[OMR Reader (Result Post Processor)|OMR Reader]]''
+[[Separation]]
 </blockquote>
-''OMR Reader'' is a '''''Post Processing''''' option for '''[[Data Type]]''' extractors.  It determines whether labeled checkboxes are checked or not and, if checked, outputs the label as its result.
+Separation, in Grooper, is the process of turning loose pages into documents, by determining points in a '''Batch''' at which '''Batch Folders''' are created and subsequent '''Batch Pages''' are placed inside.
-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.
-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).
+Pages are organized into document folders during the '''[[Separate]]''' activity.  There are a variety of methods to separate pages into documents during this activity, including (but not limited to) the use of printed control sheets, defined page lengths, and extractible text content.  The specific separation method is determined by the '''''[[Separation Provider]]''''' and its configuration used during the '''Separate''' activity.  You may also save and re-use a '''''Separation Provider's''''' configuration settings by creating a '''[[Separation Profile]]'''.
 |
 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.