AIR-WRITING RECOGNITION: MODELLING OF CHARACTERS, WORDS AND CONNECTING MOTIONS

Abstract

Air-writing refers to writing of linguistic characters or words in a free space by hand or finger movements. Air-writing differs from conventional handwriting; the latter contains the pen-up-pen-down motion, while the former lacks such a delimited sequence of writing events. Air-writing recognition problems in a pair of companion papers. In Part I, recognition of characters or words is accomplished based on six-degree-of-freedom hand motion data. We address air-writing on two levels: motion characters and motion words. Isolated air-writing characters can be recognized similar to motion gestures although with increased sophistication and variability. For motion word recognition in which letters are connected and superimposed in the same virtual box in space, we build statistical models for words by concatenating clustered ligature models and individual letter models. A hidden Markov model is used for air-writing modelling and recognition. We show that motion data along dimensions beyond a 2D trajectory can be beneficially discriminative for air-writing recognition.
The relative effectiveness of various feature dimensions of optical and inertial tracking signals and report the attainable recognition performance correspondingly. The proposed system achieves a word error rate of 0.8% for word-based recognition and 1.9% for letter-based recognition. We also subjectively and objectively evaluate the effectiveness of air-writing and compare it with text input using a virtual keyboard.