In-air handwritten English word recognition using attention recurrent translator

Abstract

As a new human–computer interaction way, in-air handwriting allows users to write in the air in a natural, unconstrained way. Compared with conventional online handwriting based on touch devices, in-air handwriting is much more challenging due to its unique characteristics. The in-air handwriting is always finished in a single stroke and thus lacks pen-down and pen-up information. Moreover, the in-air handwriting suffers less friction and space restriction so that the users write more casually. In this paper, we present an in-air handwriting system for effectively recognizing handwritten English words. An attention-based model, called attention recurrent translator, is proposed for the in-air handwritten English word recognition, which is considerably different from connectionist temporal classification (CTC). We evaluate the proposed approach on a newly collected dataset containing a total of 150,480 recordings that cover 2280 English words. The proposed approach achieves a word recognition accuracy of 97.74%. The experimental results show that the proposed recognizer is comparable with CTC and is extremely effective for in-air handwritten English word recognition.