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|[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]
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[[File:Header val property panel.png|frame|The Header-Value Extract Method's properties]]
[[File:Data-section-page-icon.png|right|link=Data Section]]
 
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<blockquote style="font-size:14pt">
''[[Header-Value]]''
'''[[Data Section]]'''
</blockquote>
</blockquote>


''Header-Value'' is one of three methods available to '''Data Table''' elements to extract information from tables on a document setIt uses a combination of column header and column value extractors to determine the table’s structure and extract information from the table’s cells.
'''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.
 
'''Data Sections''' may have, as its children:


It uses a fairly simply concept as it's basis.  You, a human reader, often know how to read a table because of the labels at the top of each column.  You know a column labeled "Date" is going to dates in each row for that column.  The "Header" part of ''Header-Value'' is establishing column header labels as the first step in modeling the table's structure.  Furthermore, if you see a column labeled "Date", you expect to see date values in the cells below.  You wouldn't expect to find a Social Security Number, for example.  That just wouldn't make sense for how the column is labeled.  This is the "Value" part of the ''Header-Value'' method.  Once you establish where the table begins, using the header labels, you can more fully model the table's structure using information about the values in each column.
* '''[[Data Field]]s'''
* '''[[Data Table]]s'''
* Their own '''Data Sections'''
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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.
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* [https://blog.bisok.com/webinars Webinars and Video]
* [https://blog.bisok.com/webinars Webinars and Video]
* [https://go.bisok.com/first-tuesday-grooper-technical-user-group First Tuesday User Group]
* [https://www.bisok.com/white-papers/ BIS White Papers]
* [https://www.bisok.com/white-papers/ BIS White Papers]
* [https://www.bisok.com/case-studies/ Case Studies]
* [https://www.bisok.com/case-studies/ Case Studies]
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* [[ACE Training Schedule]]
* [[ACE Training Schedule]]
* [https://go.bisok.com/first-tuesday-grooper-technical-user-group First Tuesday User Group Signup]
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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.

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