  This paper presents a temporal classification method for all three subtasks
of symbol segmentation, symbol recognition and relation classification in
online handwritten mathematical expressions (HMEs). The classification model is
trained by multiple paths of symbols and spatial relations derived from the
Symbol Relation Tree (SRT) representation of HMEs. The method benefits from
global context of a deep bidirectional Long Short-term Memory network, which
learns the temporal classification directly from online handwriting by the
Connectionist Temporal Classification loss. To recognize an online HME, a
symbol-level parse tree with Context-Free Grammar is constructed, where symbols
and spatial relations are obtained from the temporal classification results. We
show the effectiveness of the proposed method on the two latest CROHME
datasets.
