Handwritten numeral recognition with multiple features and multistage classifiers

Multiple expert system is shown to be a promising strategy for handwritten numeral recognition. This paper presents a multiple expert system using neural networks. In the proposed system, the authors have developed (1) an incremental clustering neural network algorithm with merging and canceling process, (2) a modified directional histogram feature extraction method and (3) a subclass method with learning rejection neuron strategy. Our experimental results on a large set of data show the efficiency and robustness of the proposed system.<<ETX>>