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Appropriate feature set for representation of pattern classes is one of themost important aspects of handwritten character recognition. The effectivenessof features depends on the discriminating power of the features chosen torepresent patterns of different classes. However, discriminatory features arenot easily measurable. Investigative experimentation is necessary foridentifying discriminatory features. In the present work we have identified anew variation of feature set which significantly outperforms on handwrittenBangla alphabet from the previously used feature set. 132 number of features inall viz. modified shadow features, octant and centroid features, distance basedfeatures, quad tree based longest run features are used here. Using thisfeature set the recognition performance increases sharply from the 75.05%observed in our previous work [7], to 85.40% on 50 character classes with MLPbased classifier on the same dataset.

An Improved Feature Descriptor for Recognition of Handwritten Bangla Alphabet