Prefix tree based auto-completion for convenient bi-modal chinese character input

We address the problem of character auto-completion (CAC) for predicting Chinese characters with partial, cursive handwriting input. A prefix tree decoder based CAC algorithm is proposed. The approach is based upon HMM pen trajectory modeling and radical structure of Chinese characters. Without finishing the strokes, high quality character candidates can be efficiently predicted. As a result, significant improvement of the recognition throughput can be obtained. We combine further the handwriting CAC with speech recognition candidates in the posterior sense, and come up with a flexible, rapid bi- modal Chinese character input system. The system was tested on a large Chinese corpus and shown that: more than 90% of input attempts can be correctly finished with only 50% of the whole character trajectory written.