Decision fusion of horizontal and vertical trajectories for recognition of online Farsi subwords

Online handwriting is formed by a combination of horizontal and vertical trajectories. If these trajectories are treated separately, new recognition methods are emerged. In contrast, one classifier is often used to recognize handwriting. In this work, some features for x(t) and y(t) signals were proposed and used to make two separate classifiers. After initial recognition by these classifiers, their results were fused for final recognition. Using HMM classifiers and simple product rule for decision fusion, the recognition results of 42 classes of Farsi subwords showed promising achievements.