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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
2.90 Reference Documentation


Featured Articles Did you know?
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Output Extractor Key

Output Extractor Key is a property on a the Data Type extractor. It is exposed when the Collation property is set to Individual. When the Output Extractor Key is set to True, each output value will be set to a key representing the name of the extractor which produced the match. It is useful when extracting non-word classification features.

The main purpose of this property is to supplement the capabilities of Grooper's classification technology. When using Lexical classification, a Content Model must use an extractor to collect the lexical features upon training. A common use case is to have the extractor collect words, which is beneficial when the semantic content of a document is varied among examples, and indicative of their type. However, this breaks down when a document consists mainly of repeated types of information. Take, for example, a bank statement. With no keywords present on the document, the only way to properly classify the document is to recognize that it contains a high frequency of transaction line items. It would be highly impractical to train Grooper to understand every variation of a transaction line item.

This is where the Output Extractor Key property comes into play. In using this property one can establish an extractor that will pattern match the various transaction line item formats on the document, and return A SINGLE output for each result, such as "feature_transaction", instead of the myriad returned results from the pattern match. This is then fed to the classification engine. With this approach a document containing a high frequency of "transaction" features, let's say ... 50, will be treated as though it contained 50 separate occurrences of the phrase "feature_transaction".

You can now manually manipulate the confidence of an extraction result. The Confidence Multiplier and Output Confidence properties of Data Type and Data Format extractors allow you to change the confidence score of extraction results. No longer are you forced to accept the score Grooper provides. These properties give you more control when it comes to what confidence a result should be.

This allows you to prioritize certain results over others. You can create a kind of "fall back" or "safety net" result by using this property. You can even increase the confidence of an extractor's result, allowing you to give more weight to a fuzzy extractor's result over a non-fuzzy one, for example.

For more information visit, the Confidence Multiplier and Output Confidence article.

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.

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Compile Stats Microsoft Office Integration Document Viewer Separation and Separation Review
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Data Review Confidence Multiplier Data Element Overrides Database Export
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CMIS Lookup Content Type Filter Output Extractor Key Box (CMIS Binding)
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LINQ to Grooper Objects
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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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