Categorization of Digital Ink Elements Using Spectral Features
نویسندگان
چکیده
It is reasonable to believe that the hand movements performed to write words are different from the types of movements performed to draw symbols. Inspired by the models that describe handwriting as a system of coupled oscillations, we believe that the different behaviours, corresponding to text, symbols, or others, present distinguished frequential patterns. Therefore, we propose a descriptor consisting of the Fourier transform of angle difference between ink segments for categorizing digital ink inputs. Preliminary experiments on a text-symbol dataset show that unsupervised clustering in the descriptor space leads to to clear categories, containing mainly text and mainly symbols, respectively.
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