Proc. SPIE. 8658, Document Recognition and Retrieval XX
KEYWORDS: Detection and tracking algorithms, Databases, Structural analysis, Computer engineering, Electrical engineering, Electronic imaging, Binary data, Rule based systems, Communication engineering, Current controlled current source
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.