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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

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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.

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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 First Tuesday User Group Signup

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-[[image:1560961400530-989.png|frame|The Infer Grid Extract Method's property panel]]
 <blockquote style="font-size:14pt">
-''[[Infer Grid]]''
+''[[Delimited Extract]]''
 </blockquote>
-''Infer Grid'' is one of three [[Table Extraction]] methods to extract data from tables on documents.  It uses the positional location of row and column headers to interpret where a tabular grid would be around each value in a table and extract values from each cell in the interpreted grid.
+''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 method extracts information by inferring a grid from the row and column header positions.  This is done by assigning an '''''X Axis Extractor''''' to match the column headers and, a '''''Y Axis Extractor''''' to match row headers.  A grid is created from the header positions extracted from the two extractors.
+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.
-Furthermore, if table line positions can be obtained from a Line Detection or Line Removal '''IP Command''', only the '''''X Axis Extractor''''' is needed.  In these cases, the '''''X Axis Extractor''''' can be used to find the column header labels, and the grid will be created using the table lines in the documents [[Layout Data]]. The raw text data obtained from the '''[[Recognize]]''' activity will populate each cell of the grid according to where it is on the page.
+{|cellpadding="10" cellspacing="5"
+|-style="background-color:#36b0a7; color:white"
+|style="font-size:14pt"|'''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.