  A classifier is developed that defines a joint distribution of global
character features, number of sub-units and local sub-unit features to model
Hindi online handwritten characters. The classifier uses latent variables to
model the structure of sub-units. The classifier uses histograms of points,
orientations, and dynamics of orientations (HPOD) features to represent
characters at global character level and local sub-unit level and is
independent of character stroke order and stroke direction variations. The
parameters of the classifier is estimated using maximum likelihood method.
Different classifiers and features used in other studies are considered in this
study for classification performance comparison with the developed classifier.
The classifiers considered are Second Order Statistics (SOS), Sub-space (SS),
Fisher Discriminant (FD), Feedforward Neural Network (FFN) and Support Vector
Machines (SVM) and the features considered are Spatio Temporal (ST), Discrete
Fourier Transform (DFT), Discrete Cosine Transform (SCT), Discrete Wavelet
Transform (DWT), Spatial (SP) and Histograms of Oriented Gradients (HOG). Hindi
character datasets used for training and testing the developed classifier
consist of samples of handwritten characters from 96 different character
classes. There are 12832 samples with an average of 133 samples per character
class in the training set and 2821 samples with an average of 29 samples per
character class in the testing set. The developed classifier has the highest
accuracy of 93.5\% on the testing set compared to that of the classifiers
trained on different features extracted from the same training set and
evaluated on the same testing set considered in this study.
