Kofax Getting Started with Ascent Xtrata Pro - Manual (Page 72)

Classification
Ascent Xtrata Pro User's Guide 53
Classification Engines and Learning by Example
The classification algorithms in Ascent Xtrata Pro can be used as classification engines.
That means that they are implemented such a way that they can easily be replaced,
and depending on the licensing an engine may or may not be available.
The following classification engines are available:
• Layout Classifier: Performs image-based classification on the image using
only graphical elements.
• Adaptive Feature Classifier (AFC): Performs content-based classification by
automatically analyzing the text created by full-text OCR or imported from
any kind of office document, for example Word files or pdf files.
• Instruction Classifier: Performs rule-base classification based on Boolean
expressions that operate on the document content.
The first two classification engines support learning by example. The only effort
required is to assign appropriate sample documents to each class. The classification
engines then execute a training process, where all the sample documents are
analyzed and important features are extracted and used to elaborate the definition of
the class in that project.
Figure 3-2. Automatic Classification
The classification engines do not need access to the training documents during
runtime. The project file contains all of the extracted information required for