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Revision as of 10:02, 23 September 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
	2.90 Reference Documentation

Featured Articles

Did you know?

Data Context

Data without context is meaningless. Context is critical to understanding and modeling the relationships between pieces of information on a document. Without context, it’s impossible to distinguish one data element from another. Context helps us understand what data refers to or “means”.

This allows us to build an extraction logic using Data Type and Field Class extractors in order to build and populate a Data Model.

There are three fundamental data context relationships:

Syntactic - Context given by the syntax of data.
Semantic - Context given by the lexical content associated with the data.
Spatial - Context given by where the data exists on the page, in relationship with other data.

Using the context these relationships provide allows us to understand how to target data with extractors.

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.

Other Resources
Grooper x Change Grooper Downloads - Including Grooper installer files Grooper.com	2.80 Reference Documentation 2.80 SDK Documentation 2.90 Reference Documentation	Webinars and Video BIS White Papers Case Studies BIS Blog	ACE Training Schedule

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-[[File:separation_and_review_14.png|right|500px|link=Separation and Separation Review|This is an example of the '''Separation Review''' '''Attended Client''' interface.]]
 <blockquote style="font-size:14pt">
-'''[[Separation and Separation Review]]'''
+[[Data Context]]
 </blockquote>
-'''Grooper''' uses various approaches and '''[https://en.wikipedia.org/wiki/Algorithm algorithms]''' to determine the classification of a page or folder. The settings on a '''[[Content Model]]''' and '''[[Document Type]]''' add to the complexities for separating pages into documents. Grooper Version 2.9 builds on the '''''Separation''''' settings found on '''Document Types''', including the ability to adjust the '''''Training Scope''''' and configure a '''''Secondary Page Extractor'''''.
+Data without context is meaningless.  Context is critical to understanding and modeling the relationships between pieces of information on a document.  Without context, it’s impossible to distinguish one data element from another.  Context helps us understand what data refers to or “means”.
-Adjusting the '''Training Scope''' provides benefits to the accuracy and performance of '''ESP Auto Separation''' by focusing what is important when it comes time to separate and classify ''Unstructured'' paginated documents.  For example, the ''Normal'' mode will create a single '''FormType''' and divide trained examples into "First", "Middle" and "Last" pages.  From individual document to individual document, often the most meaningful features composing them are found on the first and last pages, and there can be more variance on the pages in between.  This is different from the previous approach, which created individual '''FormTypes''' for each trained example, each with their own "Page X of X" '''PageType''' objects.  This unifies all trained examples into a single '''FormType''', making the training and classification of these documents ultimately simpler and more efficient.  The ''FirstLast'' mode assumes meaningful features for classification are ''only'' found on the first and last pages, with the middle pages containing no information needed to make a separation or classification decision.  With this mode enabled, ''only'' trained examples of the first and last page and their associated features will be saved.  This can improve processing time by removing all the features in the middle pages for consideration.  The ''FirstOnly'' mode narrows this scope even further by only storing features from the first page of trained documents.
+This allows us to build an extraction logic using '''[[Data Type]]''' and '''[[Field Class]]''' extractors in order to build and populate a '''[[Data Model]]'''.
-Furthermore, ESP Auto Separation ''removes'' but does not ''eliminate'' a lot of the manual work to separate and classify documents. Separation Review is a new review module designed to make the manual work quick and easy.
+There are three fundamental data context relationships:
-For more information on Separation and Separation Review, visit the full article [[Separation and Separation Review|here]]
+* '''Syntactic''' - Context given by the syntax of data.
+* '''Semantic''' - Context given by the lexical content associated with the data.
+* '''Spatial''' - Context given by where the data exists on the page, in relationship with other data.
+Using the context these relationships provide allows us to understand how to target data with extractors.
 |
-The '''Separation Review''' module was added to improve review of complicated document sets separated and classified by '''ESP Auto Separation'''.  Because this Separation Provider separates using page based classification, it can be important to how it made the decision to separate or not separate a document on a page by page basis.  The '''Classify Review''' module presents the reviewer with pages already placed in document folders, and it can be cumbersome to review the page by page separation viewing documents already placed in folders.
+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.
-Enter '''Separation Review'''.  This review module is modeled off our '''ESP Auto Separation Tester''' which Grooper Architects use to test the separation and classification of documents, using '''ESP Separation'''.  This viewer gives you a much broader look at the individual pages, allowing an easier (and ultimately quicker and more efficient) view of the separation logic applied to the batch.
+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.
-There are further quality of life improvements for the '''Separation Review''' module, making the process of reviewing documents separated and classified by '''ESP Auto Separation''' simpler, faster, and more satisfying.
+For more information visit, the [[Confidence Multiplier and Output Confidence]] article.
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 * [http://grooper.bisok.com/Documentation/2.80/Main/HTML5/index.htm#t=Start_Page.htm 2.80 Reference Documentation]
 * [http://grooper.bisok.com/Documentation/2.80/SDK/HTML5/index.htm#t=Developer_Reference.htm 2.80 SDK Documentation]
+* [https://grooper.bisok.com/Documentation/2.90/Main/HTML5/index.htm#t=Start_Page.htm 2.90 Reference Documentation]
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 * [https://blog.bisok.com/webinars Webinars and Video]