Document zone content classification and its performance evaluation

This paper describes an algorithm for the determination of zone content type of a given zone within a document image. We take a statistical based approach and represent each zone with 25 dimensional feature vectors. An optimized decision tree classifier is used to classify each zone into one of nine zone content classes. A performance evaluation protocol is proposed. The training and testing datasets include a total of 24, 177 zones from the University of Washington English Document Image database III. The algorithm accuracy is 98.45% with a mean false alarm rate of 0.50%. 

• Type: Journal
• Year: 2006
• Where: PR
• Authors: Yalin Wang, Ihsin T. Phillips, Robert M. Haralick
• Keywords: Decision Tree Classifier, Dimensional Feature Vectors, Document Image, PR 2006