Deconstructing And Restyling SVG Charts Using Large Language Models

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dc.contributor.advisorHoque Prince, Enamul
dc.contributor.authorZaidi, Syed Muhammad Ali Raza
dc.date.accessioned2025-04-10T10:48:13Z
dc.date.available2025-04-10T10:48:13Z
dc.date.copyright2024-11-15
dc.date.issued2025-04-10
dc.date.updated2025-04-10T10:48:13Z
dc.degree.disciplineInformation Systems and Technology
dc.degree.levelMaster's
dc.degree.nameMA - Master of Arts
dc.description.abstractSVG charts are very common on the Web, however, reusing, editing and restyling these charts is very difficult. To facilitate this process, this thesis explores the challenges of extracting data and visual encodings from SVG chart images and restyling them based on user queries. We leverage large language models (LLMs) to facilitate this process using few-shot prompt approaches, enabling users to deconstruct and restyle existing Vega-Lite visualizations through natural language input. Our evaluation on 800 SVG charts and 250 natural language queries reveals that our system accurately deconstruct 93.4% charts and successfully restyled 38.6% queries. Finally, based on the above techniques, we develop a Chrome plugin tool that detects and deconstructs SVG charts from the web page and then restyles the charts based on user input.
dc.identifier.urihttps://hdl.handle.net/10315/42798
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subject.keywordsData transformation and representation
dc.subject.keywordsMachine learning
dc.subject.keywordsQuantitative evaluation
dc.subject.keywordsVisual design
dc.subject.keywordsUser interfaces
dc.subject.keywordsSVG deconstruction
dc.subject.keywordsLLM image deconstruction
dc.titleDeconstructing And Restyling SVG Charts Using Large Language Models
dc.typeElectronic Thesis or Dissertation

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