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This paper proposes a novel approach to automatic recognition of handprinted Bangla (an Indian script) numerals. A modified Topology Adaptive Self-Organizing Neural Network is proposed to extract a vector skeleton from a binary numeral image. Simple heuristics are considered to prune artifacts, if any, in such a skeletal shape. Certain topological and structural features like loops, junctions, positions of terminal nodes, etc. are used along with a hierarchical tree classifier to classify handwritten numerals into smaller subgroups. Multilayer perceptron (MLP) networks are then employed to uniquely classify the numerals belonging to each subgroup. The system is trained using a sample data set of 1800 numerals and we have obtained 93.26% correct recognition rate and 1.71% rejection on a separate test set of another 7760 samples. In addition, a validation set consisting of 1440 samples has been used to determine the termination of the training algorithm of the MLP networks. The proposed scheme is sufficiently robust with respect to considerable object noise.

A new algorithm for constructing entropy reduction based decision tree classifier in two steps for large reference-class sets is proposed. The d-dimensional feature space is first mapped onto a line thus allowing a dynamic choice of features and then the resultant linear space is partitioned into two sections while minimizing the average system entropy. Classes of each section, again considered as a collection of references classes in a d-dimensional feature space, can be further split in a similar manner should the collection still be considered excessively large, thus forming a binary decision tree of nodes with overlapping members. The advantage of using such a classifier is that the need to match a test feature vector against all the references exhaustively is avoided. We demonstrate in this paper that discrete syllable recognition with dynamic programming equipped with such a classifier can reduce the recognition time by a factor of 40 to 100. The recognition speed is one third to one half of that using hidden Markov models (HMM), while the recognition rate is somewhat higher. The theory is considerably simpler than that of HMM but the decision tree can occupy a lot of memory space.