TU Dortmund Department of Computer Science LS XII Pattern Recognition GroupPublications → Publication Details

Online Bangla Word Recognition Using Sub-Stroke Level Features and Hidden Markov Models

G. A. Fink AND S. Vajda AND U. Bhattacharya AND Parui S. AND B. B. Chaudhuri
Proc. Int. Conf. on Frontiers in Handwriting Recognition, pages 393-398, Kolkata, India, 2010.

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.