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|style="width:50%"|'''Featured Article'''||'''Did you know?'''
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<blockquote>
Welcome to Grooper's Wiki!  We are constantly working to create and improve articles.  Check out a few articles we've created below!
<span style="font-size:14pt">'''[[Data Type]]'''</span>
</blockquote>
[[file:data type 1.png|frame|A sample Data Type extractor in the Node Tree]]


Data Types are [[Data Extractor]]s that use [[Regular Expression|regular expression]] to match text on a document, returning and collating the results. 
* [[OCR]]
* [[Recognize]]
* [[Table Extraction]]
* [[OMR Reader (Result Post Processor)]]
* [[Grooper Infrastructure]]


The matching pattern or patterns will return as a list of values.  The returned values can be further collated, isolated, and manipulated by configuring the properties of the Data Type.  Data Types have a variety of uses in Grooper.  Not only are they used to extract individual fields or information, but can be used to separate pages into document folders, classify documents, and more.
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|When fuzzy matching a pattern in Grooper, the regex in the lookahead and lookbehind patterns are also fuzzy matched.
[[Database Lookup]]s changed in version 2.80.  Prior to Version 2.80, database lookups were performed on individual Data Fields in a Data Model, using simple field mappings.  
 
Using standard RegEx, this pattern only matches the word "grooper" if it starts at the beginning of a new line (Using the lookahead pattern <code>\n</code>) and ends at the end of a line (using the lookbehind pattern <code>\r</code>).
 
{|style="margin:auto"
|[[File:Fuzzy lookbehind 1.png|border|250px]]||[[File:Fuzzy lookbehind 2.png|border|250px]]
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However, if you use FuzzyRegEx mode, the characters in the lookahead and lookbehind are also included as potential character swaps.
 
[[File:Fuzzy lookbehind 3.png|center|border|250px]]


In this case, the <code>\r</code> in the lookbehind was swaped for the colon character after "grooper".
Now, lookups are configured on a Data Model, Data Section or Data Table’s properties, using SQL queries. Other improvements include:
*Multiple database lookups using multiple SQL queries can be written on the Data Model.
*Lookups can reference any number of database columns and Grooper fields.
*During review, Grooper now provides a UI dialogue box when multiple matches are returned to choose the correct value.


In other words, it's not just the value pattern <code>grooper</code> that is fuzzy matched, but <code>\ngrooper\r</code> with the characters in the lookahead and lookbehind patterns included.
Visit the [[Database Lookup]] article for more information.
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Revision as of 09:54, 7 April 2020

Getting Started

Grooper is a software application that helps organizations innovate workflows by integrating difficult data.

Grooper empowers rapid innovation for organizations processing and integrating large quantities of difficult data. Created by a team of courageous developers frustrated by limitations in existing solutions, Grooper is an intelligent document and digital data integration platform. Grooper combines patented and sophisticated image processing, capture technology, machine learning, and natural language processing. Grooper – intelligent document processing; limitless, template-free data integration.

Getting Started
Install and Setup
2.80 Reference Documentation


Featured Articles Did you know?

Welcome to Grooper's Wiki! We are constantly working to create and improve articles. Check out a few articles we've created below!

Database Lookups changed in version 2.80. Prior to Version 2.80, database lookups were performed on individual Data Fields in a Data Model, using simple field mappings.

Now, lookups are configured on a Data Model, Data Section or Data Table’s properties, using SQL queries. Other improvements include:

  • Multiple database lookups using multiple SQL queries can be written on the Data Model.
  • Lookups can reference any number of database columns and Grooper fields.
  • During review, Grooper now provides a UI dialogue box when multiple matches are returned to choose the correct value.

Visit the Database Lookup article for more information.

New in 2.8 Featured Use Case

New Microfiche Processing capabilities including

Two additional batch activities

  • Recognize - Combining the old OCR and PDF Extract activities.
  • Generate PDF - Generating PDF content from processed documents, including native-PDF element creation (such as signature widgets).

Two additional IP commands

New extraction methods available to data fields

Simpler and expanded Database Lookup capabilities.

Expression based Field Mapping between data elements and their locations in external storage platforms, allowing for easier data formatting and exporting of batch processing metadata.

Data Extraction In Action: Saving Hundreds of Thousands of Dollars in 6 Months

Slowed by expensive and tedious data workflows with its current capture system, Oklahoma State University chose Grooper. They saw a quick return on investment, modernized data applications, streamlined student record processing in many ways, and can communicate with prospective students faster.

Key Outcomes

  • Reduced Months of Work in Admissions
  • Fully Automated Transcript Processing
  • Increased Enrollment Possibilities
  • Software Flexibility Leads to Cost Savings
  • Meet Project Deadlines
  • Increased Workflow Efficiency with Smart Document Classification
  • Easy Multi-Campus Deployment
  • Simplified, Economical Data Migration
  • Protection of Sensitive Information

You can access the full case study clicking this link.


Other Resources