  A set of features independent of character stroke direction and order
variations is proposed for online handwritten character recognition. A method
is developed that maps features like co-ordinates of points, orientations of
strokes at points, and dynamics of orientations of strokes at points spatially
as a function of co-ordinate values of the points and computes histograms of
these features from different regions in the spatial map.
  Different features like spatio-temporal, discrete Fourier transform, discrete
cosine transform, discrete wavelet transform, spatial, and histograms of
oriented gradients used in other studies for training classifiers for character
recognition are considered. The classifier chosen for classification
performance comparison, when trained with different features, is support vector
machines (SVM).
  The character datasets used for training and testing the classifiers consist
of online handwritten samples of 96 different Hindi characters. There are 12832
and 2821 samples in training and testing datasets, respectively.
  SVM classifiers trained with the proposed features has the highest
classification accuracy of 92.9\% when compared to the performances of SVM
classifiers trained with the other features and tested on the same testing
dataset. Therefore, the proposed features have better character discriminative
capability than the other features considered for comparison.
