The idea of combining classifiers in order to compensate their individual weakness and to preserve their individual strength has been widely used in recent pattern recognition applications. In this paper, the cooperation of two feature families for handwritten digit recognition using SKI4 (Support Vector Machine) classifiers will be examined We investigate the advantages and weaknesses of various decision fusion schemes using rule-based reasoning. The obtained results show that it is di@ult to exceed the recognition rate of the classifier applied straightforwardly on the feature families as one set. However, the rule-based cooperation schemes enable an easy and efficient implementation of various rejection criteria that leads to high reliability recognition systems.

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This paper proposes an efficient three-stage classifier for handwritten digit recognition based on NN (Neural Network) and SVM (Support Vector Machine) classifiers. The classification is performed by 2 NNs and one SVM. The first NN is designed to provide a low misclassification rate using a strong rejection criterion. It is applied on a small set of easy to extract features. Rejected patterns are forwarded to the second NN that uses additional, more complex features, and utilizes a wellbalanced rejection criterion. Finally, rejected patterns from the second NN are forwarded to an optimized SVM that considers only the "top k" classes as ranked by the NN. This way a very fast SVM classification is obtained without sacrificing the classifier accuracy. The obtained recognition rate is among the best on the MNIST database and the classification time is much better compared to the single SVM applied on the same feature set.

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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