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.
@article{arxiv.1501.05497,
title = {An Improved Feature Descriptor for Recognition of Handwritten Bangla Alphabet},
author = {Nibaran Das and Subhadip Basu and Ram Sarkar and Mahantapas Kundu and Mita Nasipuri and Dipak kumar Basu},
journal= {arXiv preprint arXiv:1501.05497},
year = {2015}
}
Comments
In proceedings of ICSIP 2009, pp. 451 to 454, August 2009, Mysore, India. arXiv admin note: substantial text overlap with arXiv:1203.0882, arXiv:1002.4040, arXiv:1410.0478