Abstract
The finger language is the part of the sign language, which is a language system that expresses vowels and consonants with hand gestures. Korean finger language has 31 gestures and each of them needs a lot of learning models for accurate recognition. If there exist mass learning models, it spend s a lot of time to search. So a real-time awareness system concentrates on how to reduce search spaces. For solving these problems, this paper suggest a hierarchy HMM structure that reduces the exploration space effectively without decreasing recognition rate. The K orea n finger language is divided into 3 categories according to the directi on of a wrist , a nd a model can be searched within these categories. Pre-classification can discern a similar finger Korean language. And it makes a search space to be managed effectively. T herefore the proposed method can be applied on the real-time recognition system. Experimental results demonstrate that the proposed method can reduce the time about three times than general HMM recognition method.
Highlights
A study on the HCI(Human-Computer interaction) internationally has been going positively
This paper suggests Korean FL HMM recognition system which is applied pre-classification, which gets data of hands on 3D using Leap Motion(6) without preprocessing or wearing equipment
As Tenchijin Keyboard is available in mobile with basic consonant and vowel of Korean, this paper is trying to recognize 31 kinds of FL, for accurate recognition and efficient manage of system resource, basic consonants and vowels are applied on Tenchijin Keyboard that recognizes 11 FL gestures (Fig 2)
Summary
A study on the HCI(Human-Computer interaction) internationally has been going positively. In case of Yamaguchi(2), there has been studied on a system that results 82% average of recognition rate using a date-glove, extracting 16 of Japan SL features from system and image which recognizes 34 FL among 46 FL. Seun-Ki Min and Hee-Deok Yang(3), has researched on study using the data-glove and method that recognizes SL and FL based on image(4~5). The second problem, for real-time recognition, is the need of efficient management on searching DB model. For solving these problems, this paper suggests Korean FL HMM recognition system which is applied pre-classification, which gets data of hands on 3D using Leap Motion(6) without preprocessing or wearing equipment
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