Online Handwritten Gurmukhi Words Recognition

Abstract

Identification of offline and online handwritten words is a challenging and complex task. In comparison to Latin and Oriental scripts, the research and study of handwriting recognition at word level in Indic scripts is at its initial phases. The two main methods of handwriting recognition are global and analytical. The present work introduces a novel analytical approach for online handwritten Gurmukhi word recognition based on a minimal set of words and recognizes an input Gurmukhi word as a sequence of characters. We employed a sequential step-by-step approach to recognize online handwritten Gurmukhi words. Considering the massive variability in online Gurmukhi handwriting, the present work employs the completely linked non-homogeneous hidden Markov model. In the present study, we considered the dependent, major-dependent, and super-dependent nature of strokes to form Gurmukhi characters in words. On test sets of online handwritten Gurmukhi datasets, the word-level accuracy rates are 85.98%, 84.80%, 82.40%, and 82.20% in four different modes. Besides the online Gurmukhi word recognition, the present work also provides Gurmukhi handwriting analysis study for varying writing styles and proposes novel techniques for zone detection and rearrangement of strokes. Our proposed algorithms have been successfully employed to online handwritten Gurmukhi word recognition in dependent and independent modes of handwriting.