InfoColorizer: Interactive Recommendation of Color Palettes for Infographics
This work provides a tool for general users to more easily create high-quality color designs for infographics, lowering the barrier for design expertise.
This paper addresses the challenge users face in selecting appropriate color palettes for infographics by proposing InfoColorizer, a data-driven method that recommends palettes considering user preferences and the spatial arrangement of elements. The tool enables users to obtain high-quality color designs with low effort, as indicated by a comprehensive four-part evaluation.
When designing infographics, general users usually struggle with getting desired color palettes using existing infographic authoring tools, which sometimes sacrifice customizability, require design expertise, or neglect the influence of elements' spatial arrangement. We propose a data-driven method that provides flexibility by considering users' preferences, lowers the expertise barrier via automation, and tailors suggested palettes to the spatial layout of elements. We build a recommendation engine by utilizing deep learning techniques to characterize good color design practices from data, and further develop InfoColorizer, a tool that allows users to obtain color palettes for their infographics in an interactive and dynamic manner. To validate our method, we conducted a comprehensive four-part evaluation, including case studies, a controlled user study, a survey study, and an interview study. The results indicate that InfoColorizer can provide compelling palette recommendations with adequate flexibility, allowing users to effectively obtain high-quality color design for input infographics with low effort.