A Bayesian model for recognizing handwritten mathematical expressions
  Recognizing handwritten mathematics is a challenging classification problem,
requiring simultaneous identification of all the symbols comprising an input as
well as the complex two-dimensional relationships between symbols and
subexpressions. Because of the ambiguity present in handwritten input, it is
often unrealistic to hope for consistently perfect recognition accuracy. We
present a system which captures all recognizable interpretations of the input
and organizes them in a parse forest from which individual parse trees may be
extracted and reported. If the top-ranked interpretation is incorrect, the user
may request alternates and select the recognition result they desire. The tree
extraction step uses a novel probabilistic tree scoring strategy in which a
Bayesian network is constructed based on the structure of the input, and each
joint variable assignment corresponds to a different parse tree. Parse trees
are then reported in order of decreasing probability. Two accuracy evaluations
demonstrate that the resulting recognition system is more accurate than
previous versions (which used non-probabilistic methods) and other academic
math recognizers.
