Prototype Pruning by Feature Extraction for Handwritten Mathematical Symbol Recognition

Successful mathematical handwriting recognition will require recognizers for large sets of handwritten symbols. This paper presents a recognition system for such handwritten mathematical symbols. The recognizer can provide a component of a handwritten interface for computer algebra systems such as Maple. Large sets of similar symbols present new challenges in the area of handwriting recognition, and we address these here. We use a pre-classification strategy, in combination with elastic matching, to improve recognition speed. Elastic matching is a model-based method that involves computation proportional to the set of candidate models. To solve this problem, we prune prototypes by examining character features. To this end, we have defined and analyzed different features. By applying these features into an elastic recognition system, the recognition speed is improved while maintaining high recognition accuracy.