Pay "attention" to Chart Images for What You Read on Text

Data visualization is changing how we understand data, by showing why's, how's, and what's behind important patterns/trends in almost every corner of the world, such as in academic papers, news articles, financial reports, etc. However, along with the increasing complexity and richness of data visualizations, given a text description (e.g., "fewer teens say they attended school completely online (8%)"), it becomes harder for users to pinpoint where to pay attention to on a chart (e.g., a grouped bar chart). In this demonstration paper, we present a system HiChart for text-chart image highlighting: when a user selects a span of text, HiChart automatically analyzes the chart image (e.g., a jpeg or a png file) and highlights the parts that are relevant to the span. From a technical perspective, HiChart devises the following techniques. Reverse-engineering visualizations: given a chart image, HiChart uses computer vision techniques to generate a visualization specification using Vega-Lite language, as well as the underlying dataset; Visualization calibration by data tuning: HiChart calibrates the re-generated chart by tuning the recovered dataset through value perturbation; and Chart highlighting for a span: HiChart maps the span to corresponding data cells and uses the built-in highlighting functions of Vega-Lite to highlight the chart.