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Text Entry for XR Trove (TEXT): Collecting and Analyzing Techniques for Text Input in XR

Arpit Bhatia, Moaaz Hudhud Mughrabi, Diar Abdlkarim, Massimiliano Di Luca, Mar Gonzalez-Franco, Karan Ahuja, Hasti Seifi

TL;DR

Text Entry for XR Trove addresses fragmentation in XR text entry by compiling a comprehensive dataset of 176 techniques and building TEXT Trove, an online tool to navigate interaction attributes and performance metrics. Using a PRISMA-inspired workflow, the authors code techniques with 32 attributes/metrics drawn from academia, industry, and hobbyists, then analyze how attributes like concurrency and input device influence speed, accuracy, and workload. Key findings show concurrency and input-device choices as dominant factors across $WPM$, $TER$, and NASA TLX, with standardized evaluation and context-aware testing emphasized for future progress. The work delivers a valuable data resource and practical guidelines that can steer the design and evaluation of XR text-entry methods toward more efficient, usable, and context-appropriate solutions.

Abstract

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.

Text Entry for XR Trove (TEXT): Collecting and Analyzing Techniques for Text Input in XR

TL;DR

Text Entry for XR Trove addresses fragmentation in XR text entry by compiling a comprehensive dataset of 176 techniques and building TEXT Trove, an online tool to navigate interaction attributes and performance metrics. Using a PRISMA-inspired workflow, the authors code techniques with 32 attributes/metrics drawn from academia, industry, and hobbyists, then analyze how attributes like concurrency and input device influence speed, accuracy, and workload. Key findings show concurrency and input-device choices as dominant factors across , , and NASA TLX, with standardized evaluation and context-aware testing emphasized for future progress. The work delivers a valuable data resource and practical guidelines that can steer the design and evaluation of XR text-entry methods toward more efficient, usable, and context-appropriate solutions.

Abstract

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.

Paper Structure

This paper contains 43 sections, 5 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Analyzing Text Input in XR
  4. Text Input Evaluation
  5. Phrase Sets
  6. Performance Metrics
  7. User Experience
  8. Factors Affecting Text Input in XR
  9. Method
  10. Identification
  11. Screening
  12. Expanding
  13. Splitting Papers Into Techniques
  14. Coming Up With Attributes and Coding
  15. Attributes and Metrics
  16. ...and 28 more sections

Figures (5)

  • Figure 1: The five stages we used to identify relevant TETs for our review. These stages are inspired by the PRISMA Flow Diagram PRISMA and include the number of papers involved in each stage.
  • Figure 2: Screenshots of Text Entry for XR Trove (TEXT). On the left, the interactive gallery of the TETs, a filter panel for searching a subset of techniques, and a suggestion form for reporting new techniques. On the right, a detailed view of a technique with its metadata, interaction attributes, and performance metrics.
  • Figure 3: WPM and TER by attribute (TETs having two attributes are plotted twice)
  • Figure 4: Overall TLX by attribute (TETs having two attributes are plotted twice)
  • Figure 5: Trends Over Time for Text Entry Techniques for XR