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|[[Install and Setup]]
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|[http://grooper.bisok.com/Documentation/2.80/Main/HTML5/index.htm#t=Start_Page.htm 2.80 Reference Documentation]
|[https://grooper.bisok.com/Documentation/2.90/Main/HTML5/index.htm#t=Start_Page.htm 2.90 Reference Documentation]
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Welcome to Grooper's Wiki! We are constantly working to create and improve articlesCheck out a few articles we've created below!
<blockquote style="font-size:14pt">
[[Data Context]]
</blockquote>
 
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:


<gallery style="text-align:center">
* '''Syntactic''' - Context given by the syntax of data.
File:What is ocr.png|link=OCR|[[OCR]]
* '''Semantic''' - Context given by the lexical content associated with the data.
File:Recognize sample.png|link=Recognize|[[Recognize]]
* '''Spatial''' - Context given by where the data exists on the page, in relationship with other data.
File:Simpletable.png|link=Table Extraction|[[Table Extraction]]
File:1555954680764-952.png|link=OMR Reader (Result Post Processor)|[[OMR Reader (Result Post Processor)]]
File:GrooperInfrastructure01.png|link=Grooper Infrastructure|[[Grooper Infrastructure]]
</gallery>


Using the context these relationships provide allows us to understand how to target data with extractors.
|
|
[[Database Lookup]]s changed in version 2.80Prior to Version 2.80, database lookups were performed on individual Data Fields in a Data Model, using simple field mappings.  
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 providesThese properties give you more control when it comes to what confidence a result ''should'' be.


Now, lookups are configured on a Data Model, Data Section or Data Table’s properties, using SQL queries. Other improvements include:
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.
*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.
For more information visit, the [[Confidence Multiplier and Output Confidence]] article.
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{|cellpadding="15" cellspacing="10"
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|-style="background-color:#f89420; color:white; font-size:16pt"
|-style="background-color:#f89420; color:white; font-size:16pt"
|style="width:50%"|'''New in 2.8'''||'''Featured Use Case'''
|style="width:50%"|'''New in 2.9'''||'''Featured Use Case'''
|-style="background-color:#fde6cb" valign="top"
|-style="background-color:#fde6cb" valign="top"
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New [[Microfiche Processing]] capabilities including
{|cellpadding=5 cellspacing=2 style="margin:auto"
* Three new batch activities specifically designed for microfiche processing
!colspan="4" style="padding: 25px" | <blockquote style="font-size:14pt">Welcome to '''Grooper 2.9'''!<br/>Below you will find helpful links to all the articles about the new/changed functionality in this version of '''Grooper'''.</blockquote>
** [[Initialize Card]]
|-
** [[Detect Frames]]
|[[Image:Compile_stats_02.png|center|200px|link=Compile Stats]]
** [[Clip Frames]]
|[[Image:Microsoft_office_integration_000.png|center|200px|link=Microsoft Office Integration]]
* Two new IP commands. While these are not strictly limited to microfiche processing they were created with microfiche processing in mind.
|[[Image:document_viewer_00.png|center|150px|link=Document Viewer]]
** [[Extract Page]]
|[[Image:Separation_and_review_18.png|center|175px|link=Separation and Separation Review]]
** [[Scratch Removal]]
|-
|style="text-align:center"|'''[[Compile Stats]]'''
|style="text-align:center"|'''[[Microsoft Office Integration]]'''
|style="text-align:center"|'''[[Document Viewer]]'''
|style="text-align:center"|'''[[Separation and Separation Review]]'''
|-
|[[Image:data_review_00.png|center|200px|link=Data Review]]
|[[Image:Weighted_rules_00.png|center|200px|link=Confidence Multiplier]]
|[[Image:Data_element_overrides_000.png|center|150px|link=Data Element Overrides]]
|[[Image:Database_export_002.png|center|200px|link=Database Export]]
|-
|style="text-align:center"|'''[[Data Review]]'''
|style="text-align:center"|'''[[Confidence Multiplier]]'''
|style="text-align:center"|'''[[Data Element Overrides]]'''
|style="text-align:center"|'''[[Database Export]]'''
|-
|[[Image:Cmis_lookup_002.png|center|200px|link=CMIS Lookup]]
|[[Image:Content_type_filter_000.png|center|100px|link=Content Type Filter]]
|[[Image:Output_extractor_key_000.png|center|200px|link=Output Extractor Key]]
|[[Image:box_cmis_binding_000.png|center|200px|link=Box (CMIS Binding)]]
|-
|style="text-align:center"|'''[[CMIS Lookup]]'''
|style="text-align:center"|'''[[Content Type Filter]]'''
|style="text-align:center"|'''[[Output Extractor Key]]'''
|style="text-align:center"|'''[[Box (CMIS Binding)]]'''
|-
|colspan="4"|[[Image:Linq_to_grooper_objects_001.png|center|200px|link=LINQ to Grooper Objects]]
|-
|colspan="4" style="text-align:center"|'''[[LINQ to Grooper Objects]]'''
|}


Two additional batch activities
|
* [[Recognize]] - Combining the old OCR and PDF Extract activities.
[[File:American-airlines-credit-union-financial-services-document-data-capture-integration-grooper.jpg|400px|right|link=https://www.bisok.com/case-studies/electronic-data-discovery-case-study/]]
* [[Generate PDF]] - Generating PDF content from processed documents, including native-PDF element creation (such as signature widgets).


Two additional IP commands
<blockquote style="font-size:14pt">
* [[Shape Removal]]
'''They’re Saving Over 5,000 Hours Every Year in Data Discovery and Processing'''
* [[Shade Removal]]
</blockquote>
 
New extraction methods available to data fields
* [[Anchored Extract]]
* [[Anchored OMR]]
* [[Find Barcode]]
* [[Read Barcode]]
* [[Zonal Extract]]


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.
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.
|
<blockquote style="font-size:14pt">
Data Extraction In Action: Saving Hundreds of Thousands of Dollars in 6 Months
</blockquote>


Slowed by expensive and tedious data workflows with its current capture
Discover how they:
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
* Quickly found 40,000 specific files among one billion
* Reduced Months of Work in Admissions
* Easily integrated with data silos and content management systems when no other solution would
* Fully Automated Transcript Processing
* Have cut their mortgage processing time in half (and they process mortgages for 47 branch offices!)
* Increased Enrollment Possibilities
* Learn from the document and electronic data discovery experts at BIS!
* 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


[https://www.bisok.com/case-studies/database-extraction-case-study/ You can access the full case study clicking this link].
[https://www.bisok.com/case-studies/electronic-data-discovery-case-study/ You can access the full case study clicking this link].
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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/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]
* [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]
|style="width:25%"|
|style="width:25%"|
* [https://blog.bisok.com/webinars Webinars and Video]
* [https://blog.bisok.com/webinars Webinars and Video]

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