An HMM framework based on spherical-linear features for online cursive handwriting recognition

In this paper a Hidden Markov Model (HMM) based writer independent online unconstrained handwritten word recognition scheme is proposed. The main steps here are segmentation of handwritten word samples into sub-strokes, feature extraction from the sub-strokes and recognition. We propose a novel but simple strategy based on the well-known discrete curve evolution for the segmentation task. Next, certain angular and linear features are extracted from the sub-strokes of word samples and are modelled as feature vectors generated from a mixture distribution. This mixture model is designed to accommodate the correlation among the angular variables. We formulate a Baum-Welch parameter estimation algorithm that can handle spherical-linear correlated data to construct an HMM. Finally, based on this HMM, we design a classifier for recognition of handwritten word samples. Simulation trials have been conducted on handwritten word sample databases of Latin and Bangla scripts demonstrating successful performance of the proposed recognition scheme.