Online Mathematical Symbol Recognition using SVMs with Features from Functional Approximation
Birendra Keshari, and Stephen M. Watt
Abstract:
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.