User-Centric Semi-Automated Infographics Authoring and Recommendation
Anjul Tyagi, Jian Zhao, Pushkar Patel, Swasti Khurana, Klaus Mueller

TL;DR
This paper introduces a flexible framework and an interactive tool for semi-automated infographic design that helps both novices and experts create diverse, high-quality infographics efficiently, supported by new datasets and evaluation results.
Contribution
It presents a novel flexible framework for infographic design, an interactive tool ame{}, and new datasets, enabling customizable and diverse infographic creation for users with varying expertise.
Findings
The framework and tool outperform similar tools in customization and diversity.
User studies show improved design quality and user satisfaction.
Datasets support diverse infographic generation and evaluation.
Abstract
Designing infographics can be a tedious process for non-experts and time-consuming even for professional designers. Based on the literature and a formative study, we propose a flexible framework for automated and semi-automated infographics design. This framework captures the main design components in infographics and streamlines the generation workflow into three steps, allowing users to control and optimize each aspect independently. Based on the framework, we also propose an interactive tool, \name{}, for assisting novice designers with creating high-quality infographics from an input in a markdown format by offering recommendations of different design components of infographics. Simultaneously, more experienced designers can provide custom designs and layout ideas to the tool using a canvas to control the automated generation process partially. As part of our work, we also…
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Taxonomy
TopicsData Visualization and Analytics · Image Retrieval and Classification Techniques · Video Analysis and Summarization
