# Beagle: Automated Extraction and Interpretation of Visualizations from the Web

arXiv: https://arxiv.org/abs/1711.05962 · HTML: https://mlsanity.com/paper/1711.05962/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Nov 2017.

## Built on by (most cited first, 19 of 19)

- [UniChart: A Universal Vision-language Pretrained Model for Chart Comprehension and Reasoning](https://mlsanity.com/paper/2305.14761/) (2023)
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- [Chart Question Answering: State of the Art and Future Directions](https://mlsanity.com/paper/2205.03966/) (2022)
- [Generative AI for Visualization: State of the Art and Future Directions](https://mlsanity.com/paper/2404.18144/) (2024)
- [AutoChart: A Dataset for Chart-to-Text Generation Task](https://mlsanity.com/paper/2108.06897/) (2021)
- [ChartInsighter: An Approach for Mitigating Hallucination in Time-series Chart Summary Generation with A Benchmark Dataset](https://mlsanity.com/paper/2501.09349/) (2025)
- [SimVecVis: A Dataset for Enhancing MLLMs in Visualization Understanding](https://mlsanity.com/paper/2506.21319/) (2025)
- [Mystique: Deconstructing SVG Charts for Layout Reuse](https://mlsanity.com/paper/2307.13567/) (2023)
- [InfoDet: A Dataset for Infographic Element Detection](https://mlsanity.com/paper/2505.17473/) (2025)
- [SuperNOVA: Design Strategies and Opportunities for Interactive Visualization in Computational Notebooks](https://mlsanity.com/paper/2305.03039/) (2023)
- [Transformers Utilization in Chart Understanding: A Review of Recent Advances & Future Trends](https://mlsanity.com/paper/2410.13883/) (2024)
- [Boosting Document Parsing Efficiency and Performance with Coarse-to-Fine Visual Processing](https://mlsanity.com/paper/2603.24326/) (2026)
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- [Document Domain Randomization for Deep Learning Document Layout Extraction](https://mlsanity.com/paper/2105.14931/) (2021)
- [An Image-based Typology for Visualization](https://mlsanity.com/paper/2403.05594/) (2024)
- [Multimodal Information Fusion for Chart Understanding: A Survey of MLLMs -- Evolution, Limitations, and Cognitive Enhancement](https://mlsanity.com/paper/2602.10138/) (2026)
- [Benchmark It Yourself (BIY): Preparing a Dataset and Benchmarking AI Models for Scatterplot-Related Tasks](https://mlsanity.com/paper/2510.06071/) (2025)
- [From Static to Interactive: Authoring Interactive Visualizations via Natural Language](https://mlsanity.com/paper/2601.17736/) (2026)
- [Chart2SVG: Editable SVG Generation from Raster Chart Images](https://mlsanity.com/paper/2608.26544/) (2026)

## Abstract (arXiv)

"How common is interactive visualization on the web?" "What is the most popular visualization design?" "How prevalent are pie charts really?" These questions intimate the role of interactive visualization in the real (online) world. In this paper, we present our approach (and findings) to answering these questions. First, we introduce Beagle, which mines the web for SVG-based visualizations and automatically classifies them by type (i.e., bar, pie, etc.). With Beagle, we extract over 41,000 visualizations across five different tools and repositories, and classify them with 86% accuracy, across 24 visualization types. Given this visualization collection, we study usage across tools. We find that most visualizations fall under four types: bar charts, line charts, scatter charts, and geographic maps. Though controversial, pie charts are relatively rare in practice. Our findings also indicate that users may prefer tools that emphasize a succinct set of visualization types, and provide diverse expert visualization examples.