User-Centric Semi-Automated Infographics Authoring and Recommendation
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Abstract (arXiv)
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 contribute an individual visual group (VG) and connection designs dataset (in SVG), along with a 1k complete infographic image dataset with segmented VGs. This dataset plays a crucial role in diversifying the infographic designs created by our framework. We evaluate our approach with a comparison against similar tools, a user study with novice and expert designers, and a case study. Results confirm that our framework and name excel in creating customized infographics and exploring a large variety of designs.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 9 of its 68 references that are in the map. First posted on arXiv 26 Aug 2021.
Builds on
9 of 68 references.
References · 9
- 2017Mask R-CNNcited by 7,259
- 2018Data2Vis: Automatic Generation of Data Visualizations Using Sequence to Sequence Recurrent Neural Networkscited by 47
- 2018VizML: A Machine Learning Approach to Visualization Recommendationcited by 38
- 2019Text-to-Viz: Automatic Generation of Infographics from Proportion-Related Natural Language Statementscited by 31
- 2020Predicting Visual Importance Across Graphic Design Typescited by 30
- 2019Towards Automated Infographic Design: Deep Learning-based Auto-Extraction of Extensible Timelinecited by 20
- 2018Synthetically Trained Icon Proposals for Parsing and Summarizing Infographicscited by 6
- 2019Task Classification Model for Visual Fixation, Exploration, and Searchcited by 5
- 2017Understanding Infographics through Textual and Visual Tag Predictioncited by 4
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Citing papers per year: 2022: 1; 2023: 1.
2 papers.