Single‐Line Drawing Vectorization

Vectorizing line drawings is a repetitive, yet necessary task that professional creatives must perform to obtain an easily editable and scalable digital representation of a raster sketch. State‐of‐the‐art automatic methods in this domain can create series of curves that closely fit the appearance of...

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Bibliographic Details
Published in:Computer graphics forum Vol. 44; no. 7
Main Authors: Magne, Tanguy, Sorkine‐Hornung, Olga
Format: Journal Article
Language:English
Published: Oxford Blackwell Publishing Ltd 01.10.2025
Subjects:
Artists
CCS Concepts
Computing methodologies → Image processing
Graph representations
Graphical representations
Neural networks
Parameterization
Parametric curve and surface models
Raster
Reconstruction
ISSN:0167-7055, 1467-8659
Online Access:Get full text
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Summary:Vectorizing line drawings is a repetitive, yet necessary task that professional creatives must perform to obtain an easily editable and scalable digital representation of a raster sketch. State‐of‐the‐art automatic methods in this domain can create series of curves that closely fit the appearance of the drawing. However, they often neglect the line parameterization. Thus, their vector representation cannot be edited naturally by following the drawing order. We present a novel method for single‐line drawing vectorization that addresses this issue. Single‐line drawings consist of a single stroke, where the line can intersect itself multiple times, making the drawing order non‐trivial to recover. Our method fits a single parametric curve, represented as a Bézier spline, to approximate the stroke in the input raster image. To this end, we produce a graph representation of the input and employ geometric priors and a specially trained neural network to correctly capture and classify curve intersections and their traversal configuration. Our method is easily extended to drawings containing multiple strokes while preserving their integrity and order. We compare our vectorized results with the work of several artists, showing that our stroke order is similar to the one artists employ naturally. Our vectorization method achieves state‐of‐the‐art results in terms of similarity with the original drawing and quality of the vectorization on a benchmark of single‐line drawings. Our method's results can be refined interactively, making it easy to integrate into professional workflows. Our code and results are available at https://github.com/tanguymagne/SLD‐Vectorization.
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ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.70228