For automatic recognition of Bangla script, only a few studies are reported in the literature, which is in contrast to the role of Bangla as one of the world's major scripts. In this paper we present a new approach to online Bangla handwriting recognition and one of the first to consider cursively written words instead of isolated characters. Our method uses a sub-stroke level feature representation of the script and a writing model based on hidden Markov models. As for the latter an appropriate internal structure is crucial, we investigate different approaches to defining model structures for a highly compositional script like Bangla. In experimental evaluations of a writer independent Bangla word recognition task we show that the use of context-dependent sub-word units achieves quite promising results and significantly outperforms alternatively structured models.
['Gernot A. Fink', 'Szilárd Vajda', 'Ujjwal Bhattacharya', 'Swapan K. Parui', 'B. B. Chaudhuri']
Online Bangla Word Recognition Using Sub-Stroke Level Features and Hidden Markov Models