SHRIMP: solving collision and out of vocabulary problems in mobile predictive input with motion gesture

Authors:
Jingtao Wang;Shumin Zhai;John Canny
Affiliations:
UC Berkeley, Berkeley, CA, USA;IBM Almaden Research Center, San Jose, CA, USA;UC Berkeley, Berkeley, CA, USA
Venue:
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Year:
2010

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Abstract

Dictionary-based disambiguation (DBD) is a very popular solution for text entry on mobile phone keypads but suffers from two problems: 1. the resolution of encoding collision (two or more words sharing the same numeric key sequence) and 2. entering out-of-vocabulary (OOV) words. In this paper, we present SHRIMP, a system and method that addresses these two problems by integrating DBD with camera based motion sensing that enables the user to express preference through a tilting or movement gesture. SHRIMP (Small Handheld Rapid Input with Motion and Prediction) runs on camera phones equipped with a standard 12-key keypad. SHRIMP maintains the speed advantage of DBD driven predictive text input while enabling the user to overcome DBD collision and OOV problems seamlessly without even a mode switch. An initial empirical study demonstrates that SHRIMP can be learned very quickly, performed immediately faster than MultiTap and handled OOV words more efficiently than DBD.