Subexpression and Dominant Symbol Histograms for Spatial Relation Classification in Mathematical Expressions
Résumé
Recognition of spatial relations between pairs of
subexpressions is a key problem of recognition of handwritten
mathematical expressions. Most methods for spatial relation classification are based on handcrafted rules and geometric indices
extracted from the subexpression bounding boxes. In this work,
we propose new spatial relation features that combine subexpression bounding box and intra-subexpression information, along
with prior knowledge about the general position and size of
symbols. Instead of handcrafting features, we train artificial
neural networks to learn the useful features from two kinds of
histograms. The first type captures the relative positions and sizes
of the subexpression bounding boxes. The second captures the
relative positions and shape of a pair of symbols, called dominant
symbols, extracted from the main baselines of the evaluated
subexpressions. We evaluate and compare our features with two
state-of-the-art features on a benchmark dataset. Experimental
results show that our features obtain better accuracy than these
two features.