Online Mathematical Symbol Recognition using SVMs with Features from Functional Approximation

We apply functional approximation techniques to obtain features from online data and use these features to train support vector machines (SVMs) for online mathematical symbol classification. We show experimental results and comparisons with another SVM-based system trained using features used in the literature. The experimental results show that the SVM trained using features from functional approximation produces results comparable to the other SVM based recognition system. This makes the functional approximation technique interesting and competitive since the features have certain computational advantages.