A Two Stage Framework for Detection and Segmentation of Writing Events in Air-Written Assamese Characters

Abstract

Gestural air-writing involves the process of writing continuous characters or words in free space using hand or finger motion. It differs from traditional pen-based writing from the fact that it does not contain delimiting points which helps in demarcation of valid writing segments. Thus, in gestural air-writing, detection of meaningful writing events from a continuous gestural sequence containing irrelevant writing movements is an intricate task which needs special attention. This paper presents an automatic method of gesture spotting and segmentation which identifies the meaningful air-written character segments confined within a continuous character pattern using a hybrid spatiotemporal and statistical feature set. A sliding window-based approach is employed for extracting the writing events from a continuous stream of hand-motion data, suppressing the superfluous idle data points. Consecutive writing events are then categorized into valid character segments and redundant ones. The relative performance of the proposed system is examined by taking various Assamese characters into consideration. Experimental results reveal that the proposed model achieves an overall segment error rate of 1.31%.