On the discriminative power of different feature subsets for handwritten numeral recognition using the box-partitioning method

This paper proposes to find an optimal feature set for handwritten numeral recognition using the box partitioning method. The feature under study is the mean normalized distance measure which is the most popular descriptor in this regard. However it is always used in combination with other descriptors and does not give good classification results when used on its own. The descriptor vector is obtained by partitioning the numeral image into sub-boxes and computing the distance measure from each sub-box taken in order. A series of evaluations is carried out in this work to verify the optimal size and number of sub-boxes by subjecting the resulting feature vectors to a rigorous handwritten numeral classification test, using a simple MLP neural network classifier. It is proved in our work that better results are obtained when the number of partitions along the horizontal and vertical axis of the image is fixed, rather than the conventional technique of arbitrarily dividing the image into sub-boxes of pre-defined dimensions.