MacLean, ScottTausky, DavidLabahn, GeorgeLank, EdwardMarzouk, MiretteCindy Grimm and Joseph J. LaViola, Jr.2014-01-282014-01-282009978-3-905674-19-41812-3503https://doi.org/10.2312/SBM/SBM09/125-132In sketch recognition systems, ground-truth data sets serve to both train and test recognition algorithms. Unfortunately, generating data sets that are sufficiently large and varied is frequently a costly and time-consuming endeavour. In this paper, we present a novel technique for creating a large and varied ground-truthed corpus for hand drawn math recognition. Candidate math expressions for the corpus are generated via random walks through a context-free grammar, the expressions are transcribed by human writers, and an algorithm automatically generates ground-truth data for individual symbols and inter-symbol relationships within the math expressions. While the techniques we develop in this paper are illustrated through the creation of a ground-truthed corpus of mathematical expressions, they are applicable to any sketching domain that can be described by a formal grammar.Categories and Subject Descriptors (according to ACM CCS): I.5.5 [Computing Methodologies]: Pattern Recognition-Implementation