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[[File:Header val property panel.png|frame|The Header-Value Extract Method's properties]]
[[image:1560961400530-989.png|frame|The Infer Grid Extract Method's property panel]]
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''[[Header-Value]]''
''[[Infer Grid]]''
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''Header-Value'' is one of three methods available to '''Data Table''' elements to extract information from tables on a document set.  It uses a combination of column header and column value extractors to determine the table’s structure and extract information from the table’s cells.
''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.


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 labeledThis 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.
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. 
 
Furthermore, if table line positions can be obtained from a Line Detection or Line Removal '''IP Command''', only the '''''X Axis Extractor''''' is neededIn 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.
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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.

Revision as of 11:32, 23 March 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?
The Infer Grid Extract Method's property panel

Infer Grid

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.

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. 

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.

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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