  This paper presents a Gaussian Mixture Model (GMM) to identify the script of
handwritten words of Roman, Devanagari, Kannada and Telugu scripts. It
emphasizes the significance of directional energies for identification of
script of the word. It is robust to varied image sizes and different styles of
writing. A GMM is modeled using a set of six novel features derived from
directional energy distributions of the underlying image. The standard
deviation of directional energy distributions are computed by decomposing an
image matrix into right and left diagonals. Furthermore, deviation of
horizontal and vertical distributions of energies is also built-in to GMM. A
dataset of 400 images out of 800 (200 of each script) are used for training GMM
and the remaining is for testing. An exhaustive experimentation is carried out
at bi-script, tri-script and multi-script level and achieved script
identification accuracies in percentage as 98.7, 98.16 and 96.91 respectively.
