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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
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Read Zone Read Zone is a Value Extractor option available to Data Fields in a Data Model. Read Zone allows you to extract text data in a rectangular region (called a "extraction zone" or just "zone") on a document. This can be a fixed zone, extracting text from the same location on a document, or a zone relative to an extracted text anchor or shape location on the document. Highly structured documents organize information into a series of data fields. These fields will have a label identifying what the field contains, such as "Name", and a corresponding value, such as "John Doe". While the values for these fields will change from document to document, their position on the document will remain constant. The Read Zone extractor extracts data using this feature of document layouts. As long as you can be reasonably assured the data you want to find will be in the same spot from document to document, you don't necessarily need anything fancier than extracting whatever text is in that known location.

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. 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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