Hybrid Mathematical Symbol Recognition Using Support Vector Machines

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 classification. Probabilistic outputs from the two support vector machine based multi-class classifiers running in parallel are combined by taking a weighted sum. Results from the experiments 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.