A novel feature extraction method and hybrid tree classification for handwritten numeral recognition

A hybrid classification system with neural network and decision tree as the classifiers for handwritten numeral recognition is proposed. Firstly a variety of stable and reliable global features are defined and extracted based on the character geometric structures, a novel floating detector is then proposed to detect segments along the left and right profiles of a character image used as local features. The recognition system consists of a hierarchical coarse classification and fine classification. For the coarse classifier: A three-layer feed forward neural network with back propagation learning algorithm is employed to distinguish six subsets {0}, {6}, {8}, {1,7}, {2,3,5}, {4,9} based on the feature similarity of characters extracted. Three character classes namely {0}, {6} and {8} are directly recognized from artificial neural network (ANN). For each of characters in the latter three subsets, a decision tree classifier is built for further fine classification as follows: Firstly, the specific feature-class relationship is heuristically and empirically deduced between the feature primitives and corresponding semantic class. Then, an iterative growing and pruning algorithm is used to form a tree classifier. Experiments demonstrated that the proposed recognition system is robust and flexible and a high recognition rate is reported.