Hybrid Mathematical Symbol Recognition Using Support Vector Machines

Authors:
B. Keshari;S. Watt
Affiliations:
University of Western Ontario;University of Western Ontario
Venue:
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
Year:
2007

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Abstract

Recognition of mathematical symbols is a challenging task, with a large set with many similar symbols. We present a support vector machine based hybrid recognition system that uses both online and offline information for classifica- tion. Probabilistic outputs from the two support vector ma- chine based multi-class classifiers running in parallel are combined by taking a weighted sum. Results from the exper- iments show that giving slightly higher weight to the on-line information produces better results. The overall error rate of the hybrid system is lower than that of both the online and offline recognition systems when used in isolation.