Kinematics Reconstruction of Static Calligraphic Traces from Curvilinear Shape Features

Our goal is to be able to reproduce computationally calligraphic traces, such as found in the art practices of graffiti and various forms of more traditional calligraphy, while mimicking their production process. To this end, we propose a method that allows to reconstruct kinematics solely from the geometric samples of handwritten traces in the form of parameters of the Sigma-Lognormal model. We ignore the kinematics possibly embedded in the data in order to treat online data and vector patterns with the same procedure.

At the heart of our method, we develop a robust procedure to identify curvilinear shape features based on an analysis of local symmetry axes. These features determine the segmentation of a trace into circular arcs and guide an iterative reconstruction of the input kinematics and geometry in the form of Sigma-Lognormal parameters. We demonstrate how this parametrisation can be used to generate plausible kinematics for a static input trace, and how parameter variations can be exploited to generate traces that resemble the ones seen in real instances of human made calligraphy and graffiti.