Document Zone Classification Using Partial Least Squares and Hybrid Classifiers

This paper introduces a novel document-zone classification algorithm. Low level image features are first extracted from document zones and partial least squares is used on pairs of classes to compute discriminating pairwise features. Rather than using the popular one-against-all and one-against-one voting schemes, we introduce a novel hybrid method which combines the benefits of the two schemes. The algorithm is applied on the University of Washington dataset and 97.3% classification accuracy is obtained.