Main Page: Difference between revisions

From Grooper Wiki
No edit summary
No edit summary
(33 intermediate revisions by 2 users not shown)
Line 4: Line 4:
|-style="background-color:#fde6cb" valign="top"
|-style="background-color:#fde6cb" valign="top"
|rowspan="3" style="width:75%"|
|rowspan="3" style="width:75%"|
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]
Line 14: Line 14:
|[https://grooper.bisok.com/Documentation/2.90/Main/HTML5/index.htm#t=Start_Page.htm 2.90 Reference Documentation]
|[https://grooper.bisok.com/Documentation/2.90/Main/HTML5/index.htm#t=Start_Page.htm 2.90 Reference Documentation]
|}
|}
{|cellpadding="15" cellspacing="10"
{|cellpadding="15" cellspacing="10"
|-style="background-color:#36b0a7; color:white; font-size:16pt"
|-style="background-color:#36b0a7; color:white; font-size:16pt"
Line 22: Line 20:
|
|
<blockquote style="font-size:14pt">
<blockquote style="font-size:14pt">
[[Expressions Cookbook]]
''[[Delimited Extract]]''
</blockquote>
</blockquote>


Expressions are snippets of .NET code, allowing Grooper to do various things outside its "normal" parameters.  This includes calculating or validating extracted '''Data Field''' values in a '''Data Model''', applying conditional execution of a '''Batch Process''' or '''IP Profile''', and more!  This article collects examples of common (and maybe not so common) uses of expressions in Grooper.
''Delimited Extract'' is one of the '''''Extract Method''''' options for '''Data Table''' objects in a '''Data Model'''.  This method extracts tabular data from a delimiter-separated text file, such as a CSV file.
 
This is the fastest, simplest and most effective method of extracting data from character delimited files, such as comma delimited CSV files or TXT files delimited by commas or other characters.
 
{|cellpadding="10" cellspacing="5"
|-style="background-color:#36b0a7; color:white"
|style="font-size:14pt"|'''FYI'''
|
|
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.
''Delimited Extract'' is new to version '''2021'''.  In older versions, this functionality was achieved with the ''CSV Extract'' table extraction method.  ''Delimited Extract'' expands the ''CSV Extract'' functionality to extract tabular data from TXT files using delimiter characters besides a comma, such as semicolons or pipe characters.
|}
|
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.
Line 33: Line 40:
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.
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.
|}
|}


{|cellpadding="15" cellspacing="10"
{|cellpadding="15" cellspacing="10"
Line 103: Line 111:
|-style="background-color:#d8f3f1" valign="center"
|-style="background-color:#d8f3f1" valign="center"
|style="width:25%"|
|style="width:25%"|
:: <div id="gartner"></div>
[[File:Gartner pi.jpg|link=https://www.gartner.com/reviews/market/data-and-analytics-others/vendor/bis/product/grooper]]
|
|
<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-integration-tools/vendor/bis?utm_source=bis&utm_medium=referral&utm_campaign=widget&utm_content=ZWYwMDE5NDAtZmNiYi00OTYyLThjY2QtNzM0MzcwMDA5NzA3 Gartner.com].<br><br>Click "Submit a review" on the image to the left to start a review.</span>
<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>
|}
|}


Line 128: Line 136:
|style="width:25%"|
|style="width:25%"|
* [[ACE Training Schedule]]
* [[ACE Training Schedule]]
* [https://go.bisok.com/first-tuesday-grooper-technical-user-group First Tuesday User Group Signup]
|}
|}



Revision as of 08:19, 9 June 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?

Delimited Extract

Delimited Extract is one of the Extract Method options for Data Table objects in a Data Model. This method extracts tabular data from a delimiter-separated text file, such as a CSV file.

This is the fastest, simplest and most effective method of extracting data from character delimited files, such as comma delimited CSV files or TXT files delimited by commas or other characters.

FYI

Delimited Extract is new to version 2021. In older versions, this functionality was achieved with the CSV Extract table extraction method. Delimited Extract expands the CSV Extract functionality to extract tabular data from TXT files using delimiter characters besides a comma, such as semicolons or pipe characters.

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