Hoque Prince, EnamulZaidi, Syed Muhammad Ali Raza2025-04-102025-04-102024-11-152025-04-10https://hdl.handle.net/10315/42798SVG 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.Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.Electronic Thesis or Dissertation2025-04-10Data transformation and representationMachine learningQuantitative evaluationVisual designUser interfacesSVG deconstructionLLM image deconstruction