A ranking-based feature selection approach for handwritten character recognition

Feature selection is generally considered a very important step in any pattern recognition process. Its aim is that of reducing the computational cost of the classification task, in an attempt to increase, or not to reduce, the classification performance. In the framework of handwriting recognition, the large variability of the handwriting of different writers makes the selection of appropriate feature sets even more complex and have been widely investigated. Although promising, the results achieved so far present several limitations, that include, among others, the computational complexity, the dependence on the adopted classifiers and the difficulty in evaluating the interactions among features. In this study, we tried to overcome some of the above drawbacks by adopting a feature-ranking-based technique: we considered different univariate measures to produce a feature ranking and we proposed a greedy search approach for choosing the feature subset able to maximize the classification results. In the experiments, we considered one of the most effective and widely used set of features in handwriting recognition to verify whether our approach allows us to obtain good classification results by selecting a reduced set of features. The experimental results, obtained by using standard real word databases of handwritten characters, confirmed the effectiveness of our proposal.