mlsanityData to 7 Oct 2026

Beagle: Automated Extraction and Interpretation of Visualizations from the Web

arXiv 1711.05962 ↗First posted 16 Nov 2017cs.HCcited by 19

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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.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Nov 2017.

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Citing papers per year: 2021: 2; 2022: 1; 2023: 3; 2024: 4; 2025: 4; 2026: 5.

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  1. 2023UniChart: A Universal Vision-language Pretrained Model for Chart Comprehension and Reasoningcited by 158
  2. 2024Advancing Multimodal Large Language Models in Chart Question Answering with Visualization-Referenced Instruction Tuningcited by 27
  3. 2022Chart Question Answering: State of the Art and Future Directionscited by 26
  4. 2024Generative AI for Visualization: State of the Art and Future Directionscited by 23
  5. 2021AutoChart: A Dataset for Chart-to-Text Generation Taskcited by 12
  6. 2025ChartInsighter: An Approach for Mitigating Hallucination in Time-series Chart Summary Generation with A Benchmark Datasetcited by 9
  7. 2025SimVecVis: A Dataset for Enhancing MLLMs in Visualization Understandingcited by 5
  8. 2023Mystique: Deconstructing SVG Charts for Layout Reusecited by 4
  9. 2025InfoDet: A Dataset for Infographic Element Detectioncited by 4
  10. 2023SuperNOVA: Design Strategies and Opportunities for Interactive Visualization in Computational Notebookscited by 3
  11. 2024Transformers Utilization in Chart Understanding: A Review of Recent Advances & Future Trendscited by 3
  12. 2026Boosting Document Parsing Efficiency and Performance with Coarse-to-Fine Visual Processingcited by 3
7 more
  1. 2026ABot-OCR Technical Reportcited by 3
  2. 2021Document Domain Randomization for Deep Learning Document Layout Extractioncited by 1
  3. 2024An Image-based Typology for Visualizationcited by 1
  4. 2026Multimodal Information Fusion for Chart Understanding: A Survey of MLLMs -- Evolution, Limitations, and Cognitive Enhancementcited by 1
  5. 2025Benchmark It Yourself (BIY): Preparing a Dataset and Benchmarking AI Models for Scatterplot-Related Taskscited by 0
  6. 2026From Static to Interactive: Authoring Interactive Visualizations via Natural Languagecited by 0
  7. 2026Chart2SVG: Editable SVG Generation from Raster Chart Imagescited by 0