Recognition technologies are being used extensively in both commercial and research systems. Recognizers are still error-prone however, and this results in performance problems and brittle dialogues, creating barriers to the acceptance and usefulness of recognition systems. Better interfaces to systems using recognition, which can help to reduce the burden of recognition errors, are difficult to build because of lack of knowledge about the ambiguity inherent in recognition. We present a survey of the design of correction techniques in interfaces which make use of recognizers. Based on this survey, we have created a user interface toolkit, OOPS (organized option pruning system) (Mankoff et al., Proceedings of ACM CHI 2000 Conference on Human Factors in Computing Systems, 2000, pp. 368-375). OOPS consists of a library of reusable error correction, or mediation, techniques drawn from the survey, combined with necessary architectural extensions to model and to provide structured support for ambiguity at the input event level of a GUI toolkit. The resulting infrastructure makes it easier for application developers to support error handling, thus helping to reduce the negative effects of recognition errors, and allowing us to explore new types of interfaces for dealing with ambiguity.

Due to the emergence of SMS messages, the significance of effective text entry on limited-size keyboards has increased. In this paper, we describe and discuss a new method to enter text more efficiently using a mobile telephone keyboard. This method, which we called HMS, predicts words from a sequence of keystrokes using a dictionary and a function combining bigram frequencies and word length. We implemented the HMS text entry method on a software-simulated mobile telephone keyboard and we...

Text input for mobile or handheld devices is a flourishing research area. This article begins with a brief history of the emergence and impact of mobile computers and mobile communications devices. Key factors in conducting sound evaluations of new technologies for mobile text entry are presented, including methodology and experiment design. Important factors to consider are identified and elaborated, such as focus of attention, text creation versus text copy tasks, novice versus expert performance, quantitative versus qualitative measures, and the speed-accuracy trade-off. An exciting area within mobile text entry is the combined use of Fitts? law and a language corpus to model, and subsequently optimize, a text entry technique. The model is described, along with examples for a variety of soft keyboards as well as the telephone keypad. A survey of mobile text entry techniques, both in research papers and in commercial products, is presented.