Kinematics Reconstruction of Static Calligraphic Traces from Curvilinear Shape Features

Berio, Daniel; Leymarie, Frederic Fol and Plamondon, Rejean. 2020. Kinematics Reconstruction of Static Calligraphic Traces from Curvilinear Shape Features. In: Rejean Plamondon; Angelo Marcelli and Miguel Ángel Ferrer, eds. The Lognormality Principle and its Applications in e-Security, e-Learning and e-Health. 88 World Scientific, pp. 237-268. ISBN 9789811226823 [Book Section]

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Abstract or Description

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.

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Chapter 11

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17 December 2020Published

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15 Mar 2021 12:17

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15 Mar 2021 12:17


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