Recognition for Large Sets of Handwritten Mathematical Symbols

Authors:
Stephen M. Watt;Xiaofang Xie
Affiliations:
University of Western Ontario, Canada;University of Western Ontario, Canada
Venue:
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Year:
2005

Citing 2
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Abstract

Natural and convenient mathematical handwriting recognition requires recognizers for large sets of handwritten symbols. This paper presents a recognition system for such handwritten mathematical symbols. 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 maintain high recognition accuracy.