An Apporach for Script Identification in Pritned Trilingual Documents Using Textural Features

Abstract

In this work, we review the outcome of texture features for script classification. Rectangular White Space analysis algorithm is used to analyze and identify heterogeneous layouts of document images. The texture features, namely the color texture moments, Local binary pattern (LBP) and responses of Gabor, LM-filter, S-filter, R-filter are extracted, and combinations of these are considered in the classification. In this work, a probabilistic neural network and Nearest Neighbor are used for classification. To corrabate the adequacy of the proposed strategy, an experiment was operated on our own data set. To study the effect of classification accuracy, we vary the database sizes and the results show that the combination of multiple features vastly improves the performance.