Off-line Recognition of Hand-Written Bengali Numerals Using Morphological Features

This paper proposes a technique for automatic recognition of Bengali handwritten numerals using multiple feature sets. We discuss about some novel Morphological features and k-curvature feature extraction technique to recognize handwritten scripts. We use different multi-layer perceptron (MLP) classifiers to train this feature spaces and then fuse those classifiers using modified `Naive´-Bayes combination to increase accuracy of recognition result. The individual feature sets give reasonably high accuracy up-to 96.25%, while fused classifier gives accuracy of 97.75%.