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Grammar-based techniques for creating ground-truthed sketch corpora

  • Scott MacLean
  • George Labahn
  • Edward Lank
  • Mirette Marzouk
  • David Tausky
Original Paper

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.

Keywords

Expression Tree Label Algorithm Terminal Symbol Symbol Recognition Nonterminal Symbol 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Scott MacLean
    • 1
  • George Labahn
    • 1
  • Edward Lank
    • 1
  • Mirette Marzouk
    • 1
  • David Tausky
    • 1
  1. 1.David R. Cheriton School of Computer ScienceUniversity of WaterlooWaterlooCanada

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