KSPC is the number of keystrokes, on average, to generate each character of text in a given language using a given text entry technique. We systematically describe the calculation of KSPC and provide examples across a variety of text entry techniques. Values for English range from about 10 for methods using only cursor keys and a SELECT key to about 0.5 for word prediction techniques. It is demonstrated that KSPC is useful for a priori analyses, thereby supporting the characterisation and comparison of text entry methods before labour-intensive implementations and evaluations.

TiltText, a new technique for entering text into a mobile phone is described. The standard 12-button text entry keypad of a mobile phone forces ambiguity when the 26letter Roman alphabet is mapped in the traditional manner onto keys 2-9. The TiltText technique uses the orientation of the phone to resolve this ambiguity, by tilting the phone in one of four directions to choose which character on a particular key to enter. We first discuss implementation strategies, and then present the results of a controlled experiment comparing TiltText to MultiTap, the most common text entry technique. The experiment included 10 participants who each entered a total of 640 phrases of text chosen from a standard corpus, over a period of about five hours. The results show that text entry speed including correction for errors using TiltText was 23% faster than MultiTap by the end of the experiment, despite a higher error rate for TiltText. TiltText is thus amongst the fastest known language-independent techniques for entering text into mobile phones.

We describe and identify shortcomings in two statistics recently introduced to measure accuracy in text entry evaluations: the minimum string distance (MSD) error rate and keystrokes per character (KSPC). To overcome the weaknesses, a new framework for error analysis is developed and demonstrated. It combines the analysis of the presented text, input stream (keystrokes), and transcribed text. New statistics include a unified total error rate, combining two constituent error rates: the corrected error rate (errors committed but corrected) and the not corrected error rate (errors left in the transcribed text). The framework includes other measures including error correction efficiency, participant conscientiousness, utilised bandwidth, and wasted bandwidth. A text entry study demonstrating the new methodology is described.

An experienced user of the Twiddler, a one–handed chording keyboard, averages speeds of 60 words per minute with letter–by–letter typing of standard test phrases. This fast typing rate coupled with the Twiddler’s 3x4 button design, similar to that of a standard mobile telephone, makes it a potential alternative to multi–tap for text entry on mobile phones. Despite this similarity, there is very little data on the Twiddler’s performance and learnability. We present a longitudinal study of novice users ’ learning rates on the Twiddler. Ten participants typed for 20 sessions using two different methods. Each session is composed of 20 minutes of typing with multi–tap and 20 minutes of one–handed chording on the Twiddler. We found that users initially have a faster average typing rate with multi–tap; however, after four sessions the difference becomes negligible, and by the eighth session participants type faster with chording on the Twiddler. Furthermore, after 20 sessions typing rates for the Twiddler are still increasing.

Six techniques for three-key text entry are described. The techniques use Left- and Right-arrow keys to maneuver a cursor over a linear sequence of characters, and a Select key to select characters. The keystrokes per character (KSPC) for the methods varies from 10.66 to 4.23. Two techniques were chosen for formal evaluation. Method #2 positions characters in alphabetical order, while Method #6 uses linguistic enhancement to reorder characters following each entry to minimize the cursor distance to the next character. Both methods position SPACE on the left and use a snap-to-home cursor mode, whereby the cursor snaps to SPACE after each entry. Entry rates were about 9-10 wpm for both techniques, as measured in an experiment with ten participants. Interaction issues are examined, such as the challenges in using linguistic knowledge to accelerate input, and the opportunity for using typamatic (viz. auto-repeat) keying strategies to reduce the number of physical keypresses.

The numeric keypads on mobile phones generally consist of 12 keys (0-9, *, #). Ambiguity arises when the 36-character alpha-numeric English alphabet is mapped onto this smaller number of keys. In this paper, we first present a taxonomy of the various techniques for resolving this ambiguity, dividing them into techniques that use consecutive actions to first select a character grouping and then a character from within that grouping, and those that use concurrent actions to achieve the same end. We then present the design and implementation of a chording approach to text entry that uses concurrent key presses. We conducted a controlled experiment that compared this chording technique to onehanded and two-handed versions of the commonly used MultiTap technique. The results show that the concurrent chording technique significantly outperforms both versions of the consecutive action MultiTap technique.

Previously, we demonstrated that after 400 minutes of practice, ten novices averaged over 26 words per minute (wpm) for text entry on the Twiddler one--handed chording keyboard, outperforming the multi--tap mobile text entry standard. Here we present an extension of this study that examines expert chording performance. Five subjects continued the study and achieved an average rate of 47 wpm after approximately 25 hours of practice in varying conditions. One subject achieved a rate of 67 wpm, equivalent to the typing rate of the last author who has been a Twiddler user for ten years. We provide evidence that lack of visual feedback does not hinder expert typing speed and examine the potential use of multiple character chords (MCCs) to increase text entry speed. We demonstrate the effects of learning on various aspects of chording and analyze how subjects adopt a simultaneous or sequential method of pushing the individual keys during a chord.

We describe how language models, combined with models of pen placement, can be used to significantly reduce the error rate of soft keyboard usage, by allowing for cases in which a key press is outside of a key boundary. Language models predict the probabilities of words or letters. Soft keyboards are images of keyboards on a touch screen used for input on Personal Digital Assistants. When a soft keyboard user hits a key near the boundary of a key position, we can use the language model and key press model to select the most probable key sequence, rather than the sequence dictated by strict key boundaries. This leads to an overall error rate reduction by a factor of 1.67 to 1.87.

Due to the emergence of SMSmessages, the significance of effective text entry on limited-size keyboards has increased.

Although text entry has been extensively studied for touch typing on standard keyboards and finger and stylus input on soft keyboards, no such work exists for two-thumb text entry on miniature Qwerty keyboards. In this paper, we propose a model for this mode of text entry. The model provides a behavioural description of the interaction as well as a predicted text entry rate in words per minute. The prediction obtained is 60.74 words per minute. The prediction is based solely on the linguistic and motor components of the task; thus, it is a peak rate for expert text entry. A detailed sensitivity analysis is included to examine the effect of changing the model's components and parameters over a broad range (+/-50% for the parameters). The model demonstrates reasonable stability --- predictions remain within about 10% of the value just cited.