Szkolenie IBM Datacap 9.0.1: Application Builder

This comprehensive course covers various aspects of Datacap application building using the FastDoc and Datacap Studio tools. It introduces the interface, and shows how to create and enhance applications from Form and Learning Templates for processing batches. You also learn about Box integration for Datacap and Datacap Cognitive Capture.

Profil uczestnika

  • Application Builders who are responsible for building Datacap applications
  • Anyone who needs to know how to rapidly develop a capture solution and enhance the application using the capability of the IBM Datacap suite


  • FastDoc Capabilities Overview
  • FastDoc local mode
    • Configure local batch profiles and parameters
    • Create a local capture batch
    • Process local batches
    • Scan, process, and verify a batch, Correct error, and Export the batch
  • Introduction to FastDoc Datacap Server mode
    • FastDoc server mode interface
    • Documents, pages, and fields view
    • Ruleset properties view
    • Workflow view and fingerprints
    • Run batches in FastDoc server mode
    • Data validation methods
    • Export options
  • Configure batch structure
    • Application Templates and Template folder structure
    • Create an application and Configure documents, pages, and fields
  • Configure form template rulesets
    • Configure rulesets
    • Batch level ruleset Import images, Convert files to images, Image enhancement
    • Recognize pages and fields
  • Define fingerprints and test rulesets
    • Define Fingerprint and select zones
    • Design time testing
    • Test pane, Profiles and rules, history and results, error resolution.
  • Populate fields with keyword lookup
  • Configure validate fields ruleset
  • Export to IBM FileNet Content Manager
  • Configure a multi-page document in a Form Template application
    • Page Separator pages
  • Data recognition
    • Bar codes (for Page Separation only)
    • OCR (optical character recognition)
    • Recognize hand written text fields ICR (intelligent character recognition)
    • OMR (optical mark recognition)
  • Validate with database lookup
  • Datacap Studio interface
    • Rulemanager View
    • Zones view
    • Test view
  • Create an application based on Learning Template
  • Configure a Learning Template application
  • Enhance the Application in Datacap Studio
    • Associate rules with a document hierarchy object
    • Associate a ruleset with a task profile
    • Validate Rules and actions
  • Create  and expand the Locate and Validation rules
  • Document export to corporate data repository
  • Application Debugging in Datacap Studio
    • Logs Used in Debugging
    • Datacap Studio Test Tab
  • Visual Studio
    • Convers Custom Control Panels
  • Connect to a Box repository
  • Configure Datacap Box connection
    • Configuration Box connection parameters
    • Configure a Datacap application for Box import and export
    • Box rulesets and action libraries
  • Introduction to Cognitive Capture
    • Terms you will encounter, Cognitive Capture, Insight Edition, Content Classification, Big Insights and System T. Alchemy API,
    • Alchemy API key application
  • Content Classification
    • Content Categorization
    • Cognitive Capture techniques
    • Cognitive Capture actions
    • IBM Content Classification
  • Insight Edition on a Datacap foundation
    • What is Insight Edition and how is it implemented?
    • What is Big Insights and System T?
    • What is in the Block?
    • What are annotators?
    • Layout Based Classification

Wymagane przygotowanie uczestników

The following course or equivalent knowledge is required:

  • IBM Datacap 9.0.1: Introduction


  • Work with FastDoc Local mode
    • Describe the FastDoc capabilities and modes of operation
    • Work with Local mode and Datacap Server mode
    • Use FastDoc as a development tool
    • Configure and run tasks in the local mode
    • Configure a Scan and Upload Task
  • Configure applications in FastDoc Server mode
    • Explore FastDoc Datacap Server mode
    • Build a new Application with the Form template
    • Create a batch structure and configure form template rulesets
    • Add a page fingerprint
    • Design time testing of ruleset
    • Populate fields with keyword lookup
    • Configure validate field rulesets
    • Configure a new page fingerprint
    • Configure export to rules for exporting to IBM FileNet Content Manager Repository.
  • Create a multi-page application based on Form Template
    • Use Page Separator pages to configure a multi-page batch
    • Recognize hand written fields with Intelligent Character Recognition ICR
    • Recognize handmade digital marks with Optical Mark Recognition OMR
    • Recognize machine print fields with Optical Character Recognition OCR
    • Validate using a lookup Database
  • Work with the Datacap Studio interface
    • Explore Rulemanager, Zones, and Test views in Datacap Studio
  • Create an application and configure with FastDoc
    • Create an application based on the Learning Template in Application Wizard
    • Configure Batch Structure (Document Hierarchy)
    • Setup Image Enhancement
    • Configure Page recognition
  • Enhance the FastDoc Application in Datacap Studio
    • Create the Locate rules to locate fields with keyword search
    • Locate fields with intellocate (automatic in the learning template)
    • Configure the field Validation rules to validate the fields on the pages
    • Extend the Locate and Validation rules to handle more page types
  • Export to IBM FileNet Content Manager repository
    • Convert document images to pdf format
    • Configure Export Rules for IBM FileNet Content Manager
  • Application debugging and Test the task profiles
    • Use the Test tab in Datacap Studio for application debugging
    • Locate and use Datacap Logs to troubleshoot Datacap error conditions
    • Visual Studio – Custom Control Panels
  • Box integration for Datacap
    • Configure a Box repository
    • Configure Datacap Box Connection
  • Cognitive Capture
    • Apply for an Alchemy key
    • Train Content Classification using Workbench
    • Classification using full page
    • Use Layout types for classification
    • Use ALCHEMY entries for classification
    • Build Page Layouts
    • Extracting Information with Text Analytics
    • Extracting Data Using Alchemy API Language
    • Filtering Results

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