An enhanced multitap text entry method with predictive next-letter highlighting

Authors:
Jun Gong;Bryan Haggerty;Peter Tarasewich
Affiliations:
Northeastern University, Boston, MA;Northeastern University, Boston, MA;Northeastern University, Boston, MA
Venue:
CHI '05 Extended Abstracts on Human Factors in Computing Systems
Year:
2005

Citing 5
Cited 4

LetterWise: prefix-based disambiguation for mobile text input

Proceedings of the 14th annual ACM symposium on User interface software and technology
The Fastap Keypad and Pervasive Computing

Pervasive '02 Proceedings of the First International Conference on Pervasive Computing
Phrase sets for evaluating text entry techniques

CHI '03 Extended Abstracts on Human Factors in Computing Systems
Reassessing current cell phone designs: using thumb input effectively

CHI '03 Extended Abstracts on Human Factors in Computing Systems
A system for fast, full-text entry for small electronic devices

Proceedings of the 5th international conference on Multimodal interfaces

Text Entry Systems: Mobility, Accessibility, Universality

Text Entry Systems: Mobility, Accessibility, Universality
Improving mobile multi-tap text entry for Arabic language

Computer Standards & Interfaces
Floodkey: increasing software keyboard keys by reducing needless ones without occultation

ACS'10 Proceedings of the 10th WSEAS international conference on Applied computer science
KeyGliss: un clavier ambigu prédictif basé sur un appui-glissé

Conference Internationale Francophone sur I'Interaction Homme-Machine

Quantified Score

Hi-index	0.00

Visualization

Abstract

Full keyboards are difficult to implement on small mobile devices, and are sometimes replaced by keypads, with multiple characters assigned to each key. The Multitap method is often used for text entry on devices with keypads. While conceptually simple, Multitap requires one or more key presses to enter each desired letter, and is relatively inefficient from the standpoint of the number of keystrokes required to enter each word. It also requires a significant amount of visual searching to find a needed letter on a key. Fortunately, newer methods based on Multitap (such as LetterWise) have been shown to increase users' text entry efficiency. This paper presents an enhanced Multitap method that uses predictive next-letter highlighting to aid visual searching. Testing shows that this method, when compared to LetterWise, offers increased text entry speeds, fewer errors, and greater novice user satisfaction.