  Appropriate feature set for representation of pattern classes is one of the
most important aspects of handwritten character recognition. The effectiveness
of features depends on the discriminating power of the features chosen to
represent patterns of different classes. However, discriminatory features are
not easily measurable. Investigative experimentation is necessary for
identifying discriminatory features. In the present work we have identified a
new variation of feature set which significantly outperforms on handwritten
Bangla alphabet from the previously used feature set. 132 number of features in
all viz. modified shadow features, octant and centroid features, distance based
features, quad tree based longest run features are used here. Using this
feature set the recognition performance increases sharply from the 75.05%
observed in our previous work [7], to 85.40% on 50 character classes with MLP
based classifier on the same dataset.
