Context based spelling correction
Information Processing and Management: an International Journal
Techniques for automatically correcting words in text
ACM Computing Surveys (CSUR)
Performance differences in the fingers, wrist, and forearm in computer input control
Proceedings of the ACM SIGCHI Conference on Human factors in computing systems
The design and evaluation of a high-performance soft keyboard
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
On-line personalization of a touch screen based keyboard
Proceedings of the 8th international conference on Intelligent user interfaces
Metrics for text entry research: an evaluation of MSD and KSPC, and a new unified error metric
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Phrase sets for evaluating text entry techniques
CHI '03 Extended Abstracts on Human Factors in Computing Systems
Language modeling for soft keyboards
Eighteenth national conference on Artificial intelligence
Correcting real-word spelling errors by restoring lexical cohesion
Natural Language Engineering
Analyzing the input stream for character- level errors in unconstrained text entry evaluations
ACM Transactions on Computer-Human Interaction (TOCHI)
Tactile feedback for mobile interactions
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Shift: a technique for operating pen-based interfaces using touch
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Investigating the effectiveness of tactile feedback for mobile touchscreens
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
One-handed touch typing on a QWERTY keyboard
Human-Computer Interaction
Usability guided key-target resizing for soft keyboards
Proceedings of the 15th international conference on Intelligent user interfaces
Predicting the cost of error correction in character-based text entry technologies
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A large scale study of text-messaging use
Proceedings of the 12th international conference on Human computer interaction with mobile devices and services
A practical examination of multimodal feedback and guidance signals for mobile touchscreen keyboards
Proceedings of the 12th international conference on Human computer interaction with mobile devices and services
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Text text revolution: a game that improves text entry on mobile touchscreen keyboards
Pervasive'11 Proceedings of the 9th international conference on Pervasive computing
A user-specific machine learning approach for improving touch accuracy on mobile devices
Proceedings of the 25th annual ACM symposium on User interface software and technology
Autonomous self-assessment of autocorrections: exploring text message dialogues
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Adaptable probabilistic flick keyboard based on HMMs
Proceedings of the companion publication of the 2013 international conference on Intelligent user interfaces companion
User-specific touch models in a cross-device context
Proceedings of the 15th international conference on Human-computer interaction with mobile devices and services
Proceedings of the 19th international conference on Intelligent User Interfaces
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Software (soft) keyboards are becoming increasingly popular on mobile devices. To attempt to improve soft keyboard input accuracy, key-target resizing algorithms that dynamically change the size of each key's target area have been developed. Although methods that employ personalized touch models have been shown to outperform general models, previous work has relied upon laboratory-based offline calibration to collect the data necessary to build these models. Such approaches are unrealistic and interuptive, and it is unlikely that offline calibration can be applied in a realistic usage setting, as hundreds or thousands of touch points are necessary to build the models. To combat this problem, this paper explores the possibility of online adaptation of key-target resizing algorithms. In particular, we propose and examine three online data collection methods that can be used to build and dynamically update personalized key-target resizing models. Our results suggest that a data collection methodology that makes inference based on vocabulary and error correction behavior is able to perform on par with gold standard personalized models, while reducing relative error rate by 10.4% over general models. This approach is simple, computationally inexpensive, and calculable via information that the system already has access to. Additionally, we show that these models can be built quickly, requiring less than one week's worth of text input by an average mobile device user.