  In this paper, we propose an automated evaluation metric for text entry. We
also consider possible improvements to existing text entry evaluation metrics,
such as the minimum string distance error rate, keystrokes per character, cost
per correction, and a unified approach proposed by MacKenzie, so they can
accommodate the special characteristics of Chinese text. Current methods lack
an integrated concern about both typing speed and accuracy for Chinese text
entry evaluation. Our goal is to remove the bias that arises due to human
factors. First, we propose a new metric, called the correction penalty (P),
based on Fitts' law and Hick's law. Next, we transform it into the approximate
amortized cost (AAC) of information theory. An analysis of the AAC of Chinese
text input methods with different context lengths is also presented.
