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.
Samanta, Oendrila; Roy, Anandarup; Parui, Swapan K.; and Bhattacharya, Ujjwal, "An HMM framework based on spherical-linear features for online cursive handwriting recognition" (2018). Journal Articles. 1405.