Shaheen, SaraRockwood, AlynGhanem, BernardChen, Min and Zhang, Hao (Richard)2016-09-272016-09-2720161467-8659https://doi.org/10.1111/cgf.12733https://diglib.eg.org:443/handle/10.1111/cgf12733Are simple strokes unique to the artist or designer who renders them? If so, can this idea be used to identify authorship or to classify artistic drawings? Also, could training methods be devised to develop particular styles? To answer these questions, we propose the Stroke Authorship Recognition (SAR) approach, a novel method that distinguishes the authorship of 2D digitized drawings. SAR converts a drawing into a histogram of stroke attributes that is discriminative of authorship. We provide extensive classification experiments on a large variety of data sets, which validate SAR's ability to distinguish unique authorship of artists and designers. We also demonstrate the usefulness of SAR in several applications including the detection of fraudulent sketches, the training and monitoring of artists in learning a particular new style and the first quantitative way to measure the quality of automatic sketch synthesis tools.Are simple strokes unique to the artist or designer who renders them? If so, can this idea be used to identify authorship or to classify artistic drawings? Also, could training methods be devised to develop particular styles? To answer these questions, we propose the Stroke Authorship Recognition (SAR) approach, a novel method that distinguishes the authorship of 2D digitized drawings. SAR converts a drawing into a histogram of stroke attributes that is discriminative of authorship.sketchstrokestroke segmentsstyleauthorship recognitionfraud detectionsketch trainingCategories and Subject Descriptors: image processing, computer vision — shape recognition10.1111/cgf.1273373-86