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Grammar-based techniques for creating ground-truthed sketch corpora

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Abstract

Although publicly available, ground-truthed corpora have proven useful for training, evaluating, and comparing recognition systems in many domains, the availability of such corpora for sketch recognizers, and math recognizers in particular, is currently quite poor. This paper presents a general approach to creating large, ground-truthed corpora for structured sketch domains such as mathematics. In the approach, random sketch templates are generated automatically using a grammar model of the sketch domain. These templates are transcribed manually, then automatically annotated with ground-truth. The annotation procedure uses the generated sketch templates to find a matching between transcribed and generated symbols. A large, ground-truthed corpus of handwritten mathematical expressions presented in the paper illustrates the utility of the approach.

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Authors and Affiliations

  1. David R. Cheriton School of Computer Science, University of Waterloo, 200 University Avenue West, Waterloo, ON, N2L 3G1, Canada

    Scott MacLean, George Labahn, Edward Lank, Mirette Marzouk & David Tausky

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  1. Scott MacLean
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  2. George Labahn
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  3. Edward Lank
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  4. Mirette Marzouk
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  5. David Tausky
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Correspondence to Scott MacLean.

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MacLean, S., Labahn, G., Lank, E. et al. Grammar-based techniques for creating ground-truthed sketch corpora. IJDAR 14, 65–74 (2011). https://doi.org/10.1007/s10032-010-0118-4

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  • DOI: https://doi.org/10.1007/s10032-010-0118-4

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