The problem of recognizing handwritten mathematics notation has been studied for over forty years with little practical success. The poor performance of math recognition systems is due, at least in part, to a lack of realistic data for use intraining recognition systems and evaluating their accuracy. In fields for which such data is available, such as face and voice recognition, the data, along with objectively-evaluated recognition contests, has contributed to the rapid advancement of the state of the art.This thesis proposes a method for constructing data corpora not only for hand-written math recognition, but for sketch recognition in general. The method consists of automatically generating template expressions, transcribing these expressions by hand, and automatically labelling them with ground-truth. This approach is motivated by practical considerations and is shown to be more extensible and objective than other potential methods.We introduce a grammar-based approach for the template generation task. In this approach, random derivations in a context-free grammar are controlled so as to generate math expressions for transcription. The generation process may be controlled in terms of expression size and distribution over mathematical semantics.Finally, we present a novel ground-truthing method based on matching terminal symbols in grammar derivations to recognized symbols. The matching is produced by a best-first search through symbol recognition results. Experiments show that this method is highly accurate but rejects many of its inputs.
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Techniques for creating ground-truthed sketch corpora
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