The metropolis keyboard - an exploration of quantitative techniques for virtual keyboard design
UIST '00 Proceedings of the 13th annual ACM symposium on User interface software and technology
Proceedings of the 8th international conference on Intelligent user interfaces
Relaxing stylus typing precision by geometric pattern matching
Proceedings of the 10th international conference on Intelligent user interfaces
Towards an intelligent multilingual keyboard system
HLT '01 Proceedings of the first international conference on Human language technology research
Eye typing using word and letter prediction and a fixation algorithm
Proceedings of the 2008 symposium on Eye tracking research & applications
Pressure-based text entry for mobile devices
Proceedings of the 11th International Conference on Human-Computer Interaction with Mobile Devices and Services
Quasi-qwerty soft keyboard optimization
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part III
Vowel-separated layout: a Thai touchscreen keyboard for people with hand movement disability
i-CREATe '11 Proceedings of the 5th International Conference on Rehabilitation Engineering & Assistive Technology
Hi-index | 0.00 |
This paper introduces an approach for improving Thai text inputting via virtual keyboard on touch screen mobile phones. We propose a technique for character candidate selection to choose the most proper character sequence. The proposed approach consists of two main parts i.e. candidate character generation based on touch area, and trigram model for candidate selection. The candidate generation is used to acquire more than one character per one touch. We define the area nearby the touch point as a touch area. The area is defined based on the statistics collected from various users. The characters within the touch area are considered as a set of candidate characters. The candidates are weighted based on their distances from the touch point. Then, the character trigram model is applied to select the combination of characters with the highest probability suggested to user. In order to evaluate the performance of the proposed technique, we evaluate the word-level accuracy of the words generated by the proposed technique and the words generated by combining the characters with the shortest distances from the touch point. Our technique yields better performance. It produces the results with the accuracy of 93.29% in character level and 66.67% in word level, while the accuracy is 83.48% in character level and 48.33% in word level when the nearest characters are selected.