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When designing infographics, general users usually struggle with gettingdesired color palettes using existing infographic authoring tools, whichsometimes sacrifice customizability, require design expertise, or neglect theinfluence of elements' spatial arrangement. We propose a data-driven methodthat provides flexibility by considering users' preferences, lowers theexpertise barrier via automation, and tailors suggested palettes to the spatiallayout of elements. We build a recommendation engine by utilizing deep learningtechniques to characterize good color design practices from data, and furtherdevelop InfoColorizer, a tool that allows users to obtain color palettes fortheir infographics in an interactive and dynamic manner. To validate ourmethod, we conducted a comprehensive four-part evaluation, including casestudies, a controlled user study, a survey study, and an interview study. Theresults indicate that InfoColorizer can provide compelling paletterecommendations with adequate flexibility, allowing users to effectively obtainhigh-quality color design for input infographics with low effort.

InfoColorizer: Interactive Recommendation of Color Palettes for Infographics