TYPE DESIGN INFORMATION PAGE last updated on Fri Dec 13 01:10:35 EST 2024
FONT RECOGNITION VIA FONT MOOSE
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Autograff
[Daniel Berio]
AutoGraff is a research project aimed at computationally modelling the perceptual and dynamic processes involved in the production of graffiti art and calligraphy. The purpose of the study is to develop computer graphics and robotic systems that are capable of generating traces, letters, and patterns that are similar to the ones made by an expert human artist. The project is driven by Daniel Berio and Frederic Fol Leymarie at the University of London. Daniel Berio is a researcher and artist from Florence, Italy. Since a young age Daniel was actively involved in the international graffiti art scene. In parallel he developed a professional career initially as a graphic designer and later as a graphics programmer in video games, multimedia and audio-visual software. In 2013 he obtained a Masters degree from the Royal Academy of Art in the Hague, where he developed drawing machines and installations materializing graffiti-inspired procedural forms. In 2021, Daniel obtained a PhD at Department of Computing Goldsmiths, University of London under the supervision of Frederic Fol Leymarie. Daniel Berio's PhD thesis is entitled AutoGraff: Towards a computational understanding of graffiti writing and related art forms. The abstract of this spectacular work that mixes art and mathematical modeling: The aim of this thesis is to develop a system that generates letters and pictures with a style that is immediately recognizable as graffiti art or calligraphy. The proposed system can be used similarly to, and in tight integration with, conventional computer-aided geometric design tools and can be used to generate synthetic graffiti content for urban environments in games and in movies, and to guide robotic or fabrication systems that can materialise the output of the system with physical drawing media. The thesis is divided into two main parts. The first part describes a set of stroke primitives, building blocks that can be combined to generate different designs that resemble graffiti or calligraphy. These primitives mimic the process typically used to design graffiti letters and exploit well known principles of motor control to model the way in which an artist moves when incrementally tracing stylised letterforms. The second part demonstrates how these stroke primitives can be automatically recovered from input geometry defined in vector form, such as the digitised traces of writing made by a user, or the glyph outlines in a font. This procedure converts the input geometry into a seed that can be transformed into a variety of calligraphic and graffiti stylisations, which depend on parametric variations of the strokes. Co-author of StrokeStyles: Stroke-based Segmentation and Stylization of Fonts (ACM Transactions on Graphics, vol. 41 (3), pp. 1-21, 2022). In this paper by Daniel Berio (Goldsmiths, University of London), Frederic Fol Leymarie (Goldsmiths, University of London), Paul Asente (Adobe Research, San Jose, CA), and Jose Echevarria (Adobe Research, San Jose, CA), the authors develop a method to automatically segment a font’s glyphs into a set of overlapping and intersecting strokes with the aim of generating artistic stylizations. The segmentation method relies on a geometric analysis of the glyph’s outline, its interior, and the surrounding areas. It uses the medial axis, curvilinear shape features that specify convex and concave outline parts, links that connect concavities, and seven junction types. We show that the resulting decomposition in strokes can be used to create variations, stylizations, and animations in different artistic or design-oriented styles while remaining recognizably similar to the input font. |
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Luc Devroye ⦿ School of Computer Science ⦿ McGill University Montreal, Canada H3A 2K6 ⦿ lucdevroye@gmail.com ⦿ https://luc.devroye.org ⦿ https://luc.devroye.org/fonts.html |