Fusion of HMM Classifiers, Based on x, y and (x, y) Signals, for the Recognition of Online Farsi Handwriting: a Large Lexicon Approach

Online handwriting is basically formed by a combination of horizontal and vertical trajectories. We employed decision fusion of semi-continuous hidden Markov model (HMM), classifiers, designed for x, y and (x, y) signals separately, to increase the recognition rate of online Farsi subwords. Because of large lexicon and few training samples for some classes, an embedded method was used to train HMM models of subwords. The result of segmentation of (x, y) signal was exploited to segment x and y signals to letters or sub-letters in the training phase. Different decision fusions of the three classifiers were investigated. The best result was achieved by applying the product rule to combine y and (x, y) classifiers.