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Comparison of Feature Performance and Its Application to Feature Combination in Off-Line Handwritten Korean Alphabet Recognition

This paper presents (1) a quantitative performance comparison of the features used in off-line handwritten Korean alphabet recognition, and (2) some experimental results based on the feature combination approach. We classified the features into three: geometrical/topological, statistical and global. For each class, we selected four or five features and performed recognition experiments with PE92 database using neural network classifiers. We compared their recognition performances and selected the top four among those thirteen features: Up Down Left Right Hole, Gradient, Structure, and Mesh. With the combination of some or all of the four features, we repeated recognition experiments. Experimental results show that (1) as a whole, geometrical/topological features outperform the other two feature classes in terms of the recognition rate, and (2) UDLRH and Gradient features in geometrical/topological feature class outperform the other features, (3) the feature combination approach can result in performance improvement.