In this paper, the cooperation of two feature families for handwritten digit recognition using a committee of Neural Network (NN) classifiers will be examined. Various cooperation schemes will be investigated and corresponding results will be presented. To improve the system reliability,we will upgrade the committee scheme using multistage classification based on rule-based and statistical cooperation. The rule-based cooperation enables an easy and efficient implementation of various rejection criteria while the statistical cooperation offers better possibility for fine-tuning of the recognition versus the reliability tradeoff. The final system has been implemented using rule-based reasoning with rejection criteria for classifier decision fusion and the generalized committee cooperation scheme for classification of the rejected digit patterns. The presented results show that we propose a successful approach for reliability control in committee classifier environment and indicate that a suitable cooperation of statistical and rule-based decision fusion is a promising approach in handwritten recognition systems.
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