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Tactile Vega-Lite: Rapidly Prototyping Tactile Charts with Smart Defaults

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arxiv 2503.00149 v2 pith:L3WFJ7WD submitted 2025-02-28 cs.HC

classification cs.HC
keywords tactilevega-litechartsdefaultsdesignersexpertdesigngraphics
verification ladder T0 review T1 audit T2 compute T3 formal
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Tactile charts are essential for conveying data to blind and low vision (BLV) readers but are difficult for designers to construct. Non-expert designers face barriers to entry due to complex guidelines, while experts struggle with fragmented and time-consuming workflows that involve extensive customization. Inspired by formative interviews with expert tactile graphics designers, we created Tactile Vega-Lite (TVL): an extension of Vega-Lite that offers tactile-specific abstractions and synthesizes existing guidelines into a series of smart defaults. Predefined stylistic choices enable non-experts to produce guideline-compliant tactile charts quickly. Expert users can override defaults to tailor customizations for their intended audience. In a user study with 12 tactile graphics creators, we show that Tactile Vega-Lite enhances flexibility and consistency by automating tasks like adjusting spacing and translating braille while accelerating iterations through pre-defined textures and line styles. Through expert critique, we also learn more about tactile chart design best practices and design decisions.

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  1. OccluNet: Spatio-Temporal Deep Learning for Occlusion Detection on DSA

    cs.CV 2025-08 unverdicted novelty 5.0 of 10

    OccluNet, a spatio-temporal attention-based detector, is claimed to outperform frame-based YOLOv11 baselines for occlusion detection in DSA sequences.

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