Chenyu Yang
ABSTRACT
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.
Supplemental Material
- Wenhu Chen, Hongmin Wang, Jianshu Chen, Yunkai Zhang, Hong Wang, Shiyang Li, Xiyou Zhou, and William Yang Wang. 2020. TabFact: A Large-scale Dataset for Table-based Fact Verification. In ICLR.Google Scholar
- Chunxiao Liu, Zhendong Mao, Tianzhu Zhang, Hongtao Xie, Bin Wang, and Yongdong Zhang. 2020. Graph Structured Network for Image-Text Matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).Google ScholarCross Ref
- Xiaoyi Liu, Diego Klabjan, and Patrick NBless. 2019. Data extraction from charts via single deep neural network. arXiv preprint arXiv:1906.11906 (2019).Google Scholar
- Junyu Luo, Zekun Li, Jinpeng Wang, and Chin-Yew Lin. 2021. ChartOCR: data extraction from charts images via a deep hybrid framework. In Proceedings of the IEEE/CVF winter conference on applications of computer vision. 1917--1925.Google ScholarCross Ref
- Ahmed Masry, Do Xuan Long, Jia Qing Tan, Shafiq Joty, and Enamul Hoque. 2022. Chartqa: A benchmark for question answering about charts with visual and logical reasoning. arXiv preprint arXiv:2203.10244 (2022).Google Scholar
- Vidya Setlur, Sarah E. Battersby, Melanie Tory, Rich Gossweiler, and Angel X. Chang. 2016. Eviza: A Natural Language Interface for Visual Analysis. In UIST. ACM, 365--377.Google Scholar
- Fangfang Zhou, Yong Zhao, Wenjiang Chen, Yijing Tan, Yaqi Xu, Yi Chen, Chao Liu, and Ying Zhao. 2021. Reverse-engineering bar charts using neural networks. Journal of Visualization 24, 2 (2021), 419--435.Google ScholarDigital Library
Index Terms
- Pay "Attention" to Chart Images for What You Read on Text
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