A structural/statistical feature based vector for handwritten character recognition
Pattern Recognition Letters
Exploiting Reliability for Dynamic Selection of Classifiers by Means of Genetic Algorithms
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Rejection Strategies Involving Classifier Combination for Handwriting Recognition
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
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A new topology for classifying decision combinations of multiple experts in the framework of a multiple expert character recognition platform is introduced. It is demonstrated that many existing multiple expert configurations for character recognition can be categorised by using this method of defining classification strategies. It is also demonstrated that the design of multiple expert character recognition configurations can be streamlined by classifying these structures in terms of how the channels used for carrying information among different experts are interconnected irrespective of the algorithms used by cooperating experts and by the final decision combination expert. Case studies of actual multiple expert character recognition configurations have been investigated and it is shown how they can be categorised with respect to the decision combination topologies introduced in the paper.