Main Page: Difference between revisions

From Grooper Wiki
No edit summary
No edit summary
(2 intermediate revisions by the same user not shown)
Line 19: Line 19:
|-style="background-color:#d8f3f1" valign="top"
|-style="background-color:#d8f3f1" valign="top"
|
|
[[File:Data-section-page-icon.png|right|link=Data Section]]
<blockquote style="font-size:14pt">
<blockquote style="font-size:14pt">
''[[Labeling Behavior]]''
'''[[Data Section]]'''
</blockquote>
</blockquote>


The ''Labeling Behavior'' is a '''Content Type''' '''''Behavior''''' designed to collect and utilize a document's field labels in a variety of ways.  This includes functionality for classification and data extraction.
'''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:


The ''Labeling Behavior'' functionality allows Grooper users to quickly onboard new '''Document Types''' for structured and semi-structured forms, utilizing labels as a thumbprint for classification and data extraction purposes.  Once the ''Labeling Behavior'' is enabled, labels are identified and collected using the "Labels" tab of '''Document Types'''. These "Label Sets" can then be used for the following purposes:
* Organize data from complex documents
* Make a hierarchical representation of a document's structure, or
* Reorder content from multiple columns on a page.


* Document classification - Using the ''Labelset-Based'' '''''Classification Method'''''
'''Data Sections''' may have, as its children:
* Field based data extraction - Using the ''Labeled Value'' '''''Extractor Type'''''
* Tabular data extraction - Using a '''Data Table''' object's ''Tabular Layout'' '''''Extract Method'''''
* Sectional data extraction - Using a '''Data Section''' object's ''Transaction Detection'' '''''Extract Method'''''


FYI: The ''Labeling Behavior'' and its functionality discussed in this article are often referred to as "Label Set Behavior" or simply "Label Sets".
* '''[[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.
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.

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:

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