Intelligent Key Prediction by N-grams and Error-correction Rules

In this paper, we propose an intelligent aid for text input method in order to provide an easier way for text inputting with the conventional keyboard. We use the character n-gram model and error correction rules to identify the language being typed and to predict the most probable character string without extra keystroke. The character n-gram model is also required only a small amount of memory spaces when using Bi-gram and Tri-gram. The paper also proposes rule-reduction algorithm applying mutual information to reduce the error-correction rules. Our algorithm archives more than 99% accuracy in both language identification and key prediction.