A novel hybrid classifier for recognition of handwritten numerals
Ping Zhang, Lihui Chen, Alex Chichung Kot · 2002
A hybrid neural network and tree classification system for handwritten numeral recognition is proposed. The recognition system consists of coarse and fine classification based on a variety of stable and reliable global features and local features. For the coarse classifier: a four-layer feedforward neural network with backpropagation learning algorithm is employed to distinguish six subsets {0}, {6}, {8}, {1,7}, {4,9}, {2,3,5} based on the similarity of character's geometrical features. Three character classes {0}, {6} and {8} are directly recognized from ANN. For each of the last three subsets, a decision tree classifier is built for fine classification as follows: firstly, the specific feature-class relationship is heuristically and empirically created 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 hybrid recognition system is robust and flexible, which can achieve a high recognition rate.