We present what we believe is the most efficient and quickest four-key text entry method available. H4-Writer uses Huffman coding to assign minimized key sequences to letters, with full access to error correction, punctuation, digits, modes, etc. The key sequences are learned quickly, and support eyes-free entry. With KSPC = 2.321, the effort to enter text is comparable to multitap on a mobile phone keypad; yet multitap requires nine keys. In a longitudinal study with six participants, an average text entry speed of 20.4 wpm was observed in the 10th session. Error rates were under 1%. To improve external validity, an extended session was included that required input of punctuation and other symbols. Entry speed dropped only by about 3 wpm, suggesting participants quickly leveraged their acquired skill with H4-Writer to access advanced features.

This paper argues that the speed-accuracy trade-off arises as a consequence of Shannon's fundamental theorem for a channel with noise, when coupled with two additional postulates: that people are imperfect information processors, and that motivation is a necessary condition of the speed-accuracy tradeoff.

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.

Text entry rates are explored for several variations of soft keyboards. We present a model to predict novice and expert entry rates and present an empirical test with 24 subjects. Six keyboards were examined: the Qwerty, ABC, Dvorak, Fitaly, JustType, and telephone. At 8-10 wpm, novice predictions are low for all layouts because the dominant factor is the visual scan time, rather than the movement time. Expert predictions are in the range of 22-56 wpm, although these were not tested empirically. In a quick, novice test with a representative phrase of text, subjects achieved rates of 20.2 wpm (Qwerty), 10.7 wpm (ABC), 8.5 wpm (Dvorak), 8.0 wpm (Fitaly), 7.0 wpm (JustType), and 8.0 wpm (telephone). The Qwerty rate of 20.2 wpm is consistent with observations in other studies. The relatively high rate for Qwerty suggests that there is skill transfer from users' familiarity with desktop computers to the stylus tapping task.

A theoretical model is presented to predict upper-and lower-bound text-entry rates using a stylus to tap on a soft QWERTY keyboard. The model is based on the Hick-Hyman law for choice reaction time, Fitts law for rapid aimed movements, and linguistic tables for the relative frequencies of letter-pairs, or digrams, in common English. The model's importance lies not only in the predictions provided, but in its characterization of text-entry tasks using keyboards. Whereas previous studies only use frequency probabilities of the 26 × 26 digrams in the Roman alphabet, our model accommodates the space har—the most common character in typing tasks. Using a very large linguistic table that decomposes digrams by position-within-words, we established start-of-word (space-letter) and end-of-word (letter-space) probabilities and worked from a 27 × 27 digram table. The model predicts a typing rate of 8.9wpm for novices unfamiliar with the QWERTY keyboard, and 30.1wpm for experts. Comparisons are drawn with empirical studies using a stylus and other forms of text entry.