Database generation and recognition of online handwritten Bangla characters

Abstract

This article describes a recent effort for database creation and recognition of online handwritten isolated basic characters of Bangla, the second most popular script of the Indian subcontinent. It describes a scheme for extraction of sub-strokes from the online samples of handwritten Bangla characters, which are significantly cursive in shapes. The proposed recognition scheme includes a new feature vector to be computed for each sub-stroke. The recognition performance of the test samples of the present database is evaluated separately on two classifiers -- a Hidden Markov Model (HMM) based classifier and a nearest-neighbour classifier based on Dynamic Time Warping (DTW). The second classifier outperforms the HMM-based classifier for the present test set.