Abstract 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.

Restoring the original, flat appearance of a printed document from casual photographs of bent and wrinkled pages is a common everyday problem. In this paper we propose a novel method for grid-based single-image document unwarping. Our method performs geometric distortion correction via a fully convolutional deep neural network that learns to predict the 3D grid mesh of the document and the corresponding 2D unwarping grid in a dual-task fashion, implicitly encoding the coupling between the shape of a 3D piece of paper and its 2D image. In order to allow unwarping models to train on data that is more realistic in appearance than the commonly used synthetic Doc3D dataset, we create and publish our own dataset, called UVDoc, which combines pseudo-photorealistic document images with physically accurate 3D shape and unwarping function annotations. Our dataset is labeled with all the information necessary to train our unwarping network, without having to engineer separate loss functions that can deal with the lack of ground-truth typically found in document in the wild datasets. We perform an in-depth evaluation that demonstrates that with the inclusion of our novel pseudo-photorealistic dataset, our relatively small network architecture achieves state-of-the-art results on the DocUNet benchmark. We show that the pseudo-photorealistic nature of our UVDoc dataset allows for new and better evaluation methods, such as lighting-corrected MS-SSIM. We provide a novel benchmark dataset that facilitates such evaluations, and propose a metric that quantifies line straightness after unwarping. Our code, results and UVDoc dataset are available at https://github.com/tanguymagne/UVDoc.