Comparison of Zone-Features for Online Bengali and Devanagari Word Recognition Using HMM

Abstract

This paper presents a comparative study of three feature extraction approaches for online handwritten word recognition of two major Indic scripts-Bengali and Devanagari using Hidden Markov Model (HMM). First approach uses feature extraction from whole stroke without local zone division after segmenting the word into its basic strokes. Whereas, other two approaches consider the segmentation of a word into its basic strokes and a local zone wise analysis of each online stroke. Among these two zone wise local features, one takes into account structural and directional features and other uses dominant points, detected from strokes using slope angles, to find the local features. These features are studied in HMM-based word recognition platform. From the comparative study of the word recognition results, we have noted that dominant point based local feature extraction provides best accuracies for both Bengali and Devanagari scripts. We have obtained 90.23% and 93.82% accuracies for Bengali and Devanagari scripts respectively.