Text Entry for XR Trove (TEXT): Collecting and Analyzing Techniques for Text Input in XR
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909537349402624 |
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| author | Bhatia, Arpit Mughrabi, Moaaz Hudhud Abdlkarim, Diar Di Luca, Massimiliano Gonzalez-Franco, Mar Ahuja, Karan Seifi, Hasti |
| author_facet | Bhatia, Arpit Mughrabi, Moaaz Hudhud Abdlkarim, Diar Di Luca, Massimiliano Gonzalez-Franco, Mar Ahuja, Karan Seifi, Hasti |
| contents | Text entry for extended reality (XR) is far from perfect, and a variety of text entry techniques (TETs) have been proposed to fit various contexts of use. However, comparing between TETs remains challenging due to the lack of a consolidated collection of techniques, and limited understanding of how interaction attributes of a technique (e.g., presence of visual feedback) impact user performance. To address these gaps, this paper examines the current landscape of XR TETs by creating a database of 176 different techniques. We analyze this database to highlight trends in the design of these techniques, the metrics used to evaluate them, and how various interaction attributes impact these metrics. We discuss implications for future techniques and present TEXT: Text Entry for XR Trove, an interactive online tool to navigate our database. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_11357 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Text Entry for XR Trove (TEXT): Collecting and Analyzing Techniques for Text Input in XR Bhatia, Arpit Mughrabi, Moaaz Hudhud Abdlkarim, Diar Di Luca, Massimiliano Gonzalez-Franco, Mar Ahuja, Karan Seifi, Hasti Human-Computer Interaction Text entry for extended reality (XR) is far from perfect, and a variety of text entry techniques (TETs) have been proposed to fit various contexts of use. However, comparing between TETs remains challenging due to the lack of a consolidated collection of techniques, and limited understanding of how interaction attributes of a technique (e.g., presence of visual feedback) impact user performance. To address these gaps, this paper examines the current landscape of XR TETs by creating a database of 176 different techniques. We analyze this database to highlight trends in the design of these techniques, the metrics used to evaluate them, and how various interaction attributes impact these metrics. We discuss implications for future techniques and present TEXT: Text Entry for XR Trove, an interactive online tool to navigate our database. |
| title | Text Entry for XR Trove (TEXT): Collecting and Analyzing Techniques for Text Input in XR |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2503.11357 |