Visual System of Sign Alphabet Learning for Poorly-Hearing Children
Training visual systems have significant role for people with limited physical abilities. In this paper, the task of sign alphabet learning by poorly-hearing children was discussed using advanced recognition methods. Such intelligent system is an additional instrument for cultural development of children who can not learn alphabet in the usual way. The novelty of the method consists in proposed technique of features extraction and building vector models of outer contours for following identification of gestures which are associated with letters. The high variability of gestures in 3D space causes ambiguous segmentation, which makes the visual normalization necessary. The corresponding software has two modes: a learning mode (building of etalon models) and a testing mode (recognition of a current gesture). The Visual system of Russian sign alphabet learning is a real-time application and does not need high computer resources.
KeywordsSign alphabet gesture recognition features extraction spatiotemporal segmentation skin classifiers
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