Word-level script identification from handwritten multi-script documents

Abstract

In this paper, a robust word-level handwritten script identification technique has been proposed. A combination of shape based and texture based features are used to identify the script of the handwritten word images written in any of five scripts namely, Bangla, Devnagari, Malayalam, Telugu and Roman. An 87-element feature set is designed to evaluate the present script recognition technique. The technique has been tested on 3000 handwritten words in which each script contributes about 600 words. Based on the identification accuracies of multiple classifiers, Multi Layer Perceptron (MLP) has been chosen as the best classifier for the present work. For 5-fold cross validation and epoch size of 500, MLP classifier produces the best recognition accuracy of 91.79% which is quite impressive considering the shape variations of the said scripts.