Combining multiple classifiers based on a statistical method for handwritten chinese character recognition

Combining multiple classifiers is a new method that achieves a substantial gain in performance in many areas of pattern recognition. This paper demonstrates a novel method (based on statistics) of combining multiple classifiers to address the task of recognizing handwritten Chinese characters. Fusion strategies are discussed to provide a basis for the architecture of the combined classifiers. The weights of these fusion strategies are assigned via a genetic algorithm (GA). These fusion strategies are then tested using our online system for handwritten Chinese character recognition. In addition, different combinatory approaches are tested for comparison purposes. These include the conventional approach that is based on the Bayesian principle and the improved weighted combination, employing shared and distinct representations. Our experimental results demonstrate the effectiveness of these combinatory approaches.