Main Page: Difference between revisions

Revision as of 13:04, 28 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?

Data Section

Data Sections are Data Elements of a Data Model. They allow a document's content to be subdivided into smaller portions (or "sections") for further processing, yielding the extraction process higher efficiency and accuracy.

Often, they are used to extract repeating sections of a document. For example, if a document had several sections of data for different customers, a Data Section could be used to pull data for each customer. This is especially useful for situations where the data within the section is predictable, but the number of sections in the document is not (i.e. if one document has one customer's data listed but the next has five, the next has two, and so on and so on).

Data Sections can also be used to:

Organize data from complex documents
Make a hierarchical representation of a document's structure, or
Reorder content from multiple columns on a page.

Data Sections may have, as its children:

Data Fields
Data Tables
Their own Data Sections

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 First Tuesday User Group Signup

@@ Line 14: / Line 14: @@
 |[https://grooper.bisok.com/Documentation/2.90/Main/HTML5/index.htm#t=Start_Page.htm 2.90 Reference Documentation]
 |}
 {|cellpadding="15" cellspacing="10"
 |-style="background-color:#36b0a7; color:white; font-size:16pt"
@@ Line 21: / Line 19: @@
 |-style="background-color:#d8f3f1" valign="top"
 |
-[[image:1560961400530-989.png|frame|The Infer Grid Extract Method's property panel]]
+[[File:Data-section-page-icon.png|right|link=Data Section]]
 <blockquote style="font-size:14pt">
-''[[Infer Grid]]''
+'''[[Data Section]]'''
 </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.
+'''Data Sections''' are '''[[Data Element]]s''' of a '''[[Data Model]]'''.  They allow a document's content to be subdivided into smaller portions (or "sections") for further processing, yielding the extraction process higher efficiency and accuracy.
+Often, they are used to extract repeating sections of a document.  For example, if a document had several sections of data for different customers, a '''Data Section''' could be used to pull data for each customer.  This is especially useful for situations where the data within the section is predictable, but the number of sections in the document is not (i.e. if one document has one customer's data listed but the next has five, the next has two, and so on and so on).
+'''Data Sections''' can also be used to:
+* Organize data from complex documents
+* Make a hierarchical representation of a document's structure, or
+* Reorder content from multiple columns on a page.
-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.
+'''Data Sections''' may have, as its children:
-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.
+* '''[[Data Field]]s'''
+* '''[[Data Table]]s'''
+* Their own '''Data Sections'''
 |
 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.