Structural analysis of online handwritten mathematical symbols based on support vector machines

Foteini Simistira; Vassilis Papavassiliou; Vassilis Katsouros; George Carayannis

doi:10.1117/12.2003112

4 February 2013 Structural analysis of online handwritten mathematical symbols based on support vector machines

Foteini Simistira, Vassilis Papavassiliou, Vassilis Katsouros, George Carayannis

Proceedings Volume 8658, Document Recognition and Retrieval XX; 86580Z (2013) https://doi.org/10.1117/12.2003112
Event: IS&T/SPIE Electronic Imaging, 2013, Burlingame, California, United States

Abstract

Mathematical expression recognition is still a very challenging task for the research community mainly because of the two-dimensional (2d) structure of mathematical expressions (MEs). In this paper, we present a novel approach for the structural analysis between two on-line handwritten mathematical symbols of a ME, based on spatial features of the symbols. We introduce six features to represent the spatial affinity of the symbols and compare two multi-class classification methods that employ support vector machines (SVMs): one based on the “one-against-one” technique and one based on the “one-against-all”, in identifying the relation between a pair of symbols (i.e. subscript, numerator, etc). A dataset containing 1906 spatial relations derived from the Competition on Recognition of Online Handwritten Mathematical Expressions (CROHME) 2012 training dataset is constructed to evaluate the classifiers and compare them with the rule-based classifier of the ILSP-1 system participated in the contest. The experimental results give an overall mean error rate of 2.61% for the “one-against-one” SVM approach, 6.57% for the “one-against-all” SVM technique and 12.31% error rate for the ILSP-1 classifier.

Citation Download Citation

Foteini Simistira, Vassilis Papavassiliou, Vassilis Katsouros, and George Carayannis "Structural analysis of online handwritten mathematical symbols based on support vector machines", Proc. SPIE 8658, Document Recognition and Retrieval XX, 86580Z (4 February 2013); https://doi.org/10.1117/12.2003112

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