HMM-Based Lexicon-Driven and Lexicon-Free Word Recognition for Online Handwritten Indic Scripts

TLDR: This paper proposes two different techniques for word recognition based on Hidden Markov Models (HMM): lexicon driven and lexicon free, which significantly outperforms either of them used in isolation on handwritten Devanagari word samples.

Abstract: Research for recognizing online handwritten words in Indic scripts is at its early stages when compared to Latin and Oriental scripts In this paper, we address this problem specifically for two major Indic scripts-Devanagari and Tamil In contrast to previous approaches, the techniques we propose are largely data driven and script independent We propose two different techniques for word recognition based on Hidden Markov Models (HMM): lexicon driven and lexicon free The lexicon-driven technique models each word in the lexicon as a sequence of symbol HMMs according to a standard symbol writing order derived from the phonetic representation The lexicon-free technique uses a novel Bag-of-Symbols representation of the handwritten word that is independent of symbol order and allows rapid pruning of the lexicon On handwritten Devanagari word samples featuring both standard and nonstandard symbol writing orders, a combination of lexicon-driven and lexicon-free recognizers significantly outperforms either of them used in isolation In contrast, most Tamil word samples feature the standard symbol order, and the lexicon-driven recognizer outperforms the lexicon free one as well as their combination The best recognition accuracies obtained for 20,000 word lexicons are 8713 percent for Devanagari when the two recognizers are combined, and 918 percent for Tamil using the lexicon-driven technique