Using the Recognition Strategy Library to Study the Behavior of a Math Recognition System

نویسندگان

  • Kevin Talmadge
  • Richard Zanibbi
چکیده

We instrument a system for recognizing handwritten mathematical expressions using the Recognition Strategy Library, a data provenance tool for pattern recognition research, in order to study the behavior of the recognition system and demonstrate the utility of the library. In this case study, the library was used to perform comparison tests to quantitatively analyze several modifications to the recognition system. Several improvements were made to the library as a result of this study, including the ability to obtain the set of unique interpretations, determine the intersection and difference between two interpretation sets, and adding a method to dynamically record interpretations within a decision function.

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تاریخ انتشار 2014