An Improved Feature Descriptor for Recognition of Handwritten Bangla Alphabet

TL;DR

This paper introduces a new feature set for handwritten Bangla alphabet recognition, significantly improving accuracy from 75.05% to 85.40% using an MLP classifier.

Contribution

The paper proposes a novel combination of 132 features that enhances recognition performance over previous feature sets for handwritten Bangla characters.

Findings

Recognition accuracy improved to 85.40%

New feature set outperforms previous methods

Effective for handwritten Bangla alphabet recognition

Abstract

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.