Demo Data from SIGIR 2026 / reviewers in the wild / expert
Mustapha Benaouicha
dblp:118/8152
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
0000-0002-5332-2439ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Human-computer interaction and pervasive computing
1 paper |
Interaction techniques and input · 100% | |
| Computer graphics and multimedia
1 paper |
Virtual and augmented reality · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Interaction techniques and input
text entry |
0.7 | 1 | 2023 | Handwriting for Efficient Text Entry in Industrial VR Applications: Influence of Board Orientation and Sensory Feedback on Performance · IEEE Trans. Vis. Comput. Graph. 2023 |
Virtual and augmented reality › virtual reality
industrial VR application |
0.2 | 1 | 2023 | Handwriting for Efficient Text Entry in Industrial VR Applications: Influence of Board Orientation and Sensory Feedback on Performance · IEEE Trans. Vis. Comput. Graph. 2023 |
Methods — techniques the papers use, named apart from their topics
user study · 1.3handwriting recognition · 1.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Handwriting for Efficient Text Entry in Industrial VR Applications: Influence of Board Orientation and Sensory Feedback on PerformanceabstractText entry in Virtual Reality (VR) is becoming an increasingly important task as the availability of hardware increases and the range of VR applications widens. This is especially true for VR industrial applications where users need to input data frequently. Large-scale industrial adoption of VR is still hampered by the productivity gap between entering data via a physical keyboard and VR data entry methods. Data entry needs to be efficient, easy-to-use and to learn and not frustrating. In this paper, we present a new data entry method based on handwriting recognition (HWR). Users can input text by simply writing on a virtual surface. We conduct a user study to determine the best writing conditions when it comes to surface orientation and sensory feedback. This feedback consists of visual, haptic, and auditory cues. We find that using a slanted board with sensory feedback is best to maximize writing speeds and minimize physical demand. We also evaluate the performance of our method in terms of text entry speed, error rate, usability and workload. The results show that handwriting in VR has high entry speed, usability with little training compared to other controller-based virtual text entry techniques. The system could be further improved by reducing high error rates through the use of more efficient handwriting recognition tools. In fact, the total error rate is 9.28% in the best condition. After 40 phrases of training, participants reach an average of 14.5 WPM, while a group with high VR familiarity reach 16.16 WPM after the same training. The highest observed textual data entry speed is 21.11 WPM. Nicolas Fourrier, Guillaume Moreau, Mustapha Benaouicha, Jean-Marie Normand |
IEEE Trans. Vis. Comput. Graph. | 3 |