This means that even if all the characters are correctly recognized, there still remains the nontrivial problem of interpreting the structure of the expression. Hence, it is difficult to apply the Markovian methods which have been applied to parsing one-dimensional texts such as standard prose or programming languages. One common approach is to model the grammatical nature of mathematics in one way or another. System Description We divide the recognition problem into three modular subproblems called isolated symbol classification , stroke set partitioning , and parsing . This division has the advantage that each subproblem can be solved and its performance evaluated essentially independent of the others, so that improvements can be made in each area while still maintaining the integrity of the entire system.
Recognition is performed in two discrete stages, indicated in the diagram below.
First, the strokes are partitioned into symbols by the stroke partitioner. Then, the identity of the symbols and the structure of the expression is determined by the parser. Symbol models, estimated from examples of handwriting, are used by the partitioner in order to decide how likely it that a particular set of strokes constitutes a distinct symbol. After recognition is complete, the user interface displays the resulting expression. One important aspect of this division is that hard decisions are made at each stage, so that new information cannot change the results of earlier processing.