Abstract
In this paper, we present the development of a simple and low cost data glove system using tilt and flex sensors as a Korean Finger Spelling (KFS) recognition system. This data glove has the capability to measure the palm and finger gesture postures. The process of building a simple KFS recognition system and method for recognizing the KFS letters is also proposed in this paper. The k-means algorithm is used to classify the KFS letter’s based on tilt sensor measurement. The flex sensor measurement on each finger is divided into three main bending positions and quantization index rule-based is used to recognize the KFS letters. For the convenience of using this glove, a simple and efficient calibration process of the finger gesture is provided, so that all the required parameters for recognition can be adapted automatically. The system gives an average of 80% correct recognition for the 24 letters in KFS. The glove-based KFS is possibility to ease and encourage the Korean community to learn KFS by providing hands-on and minds-on learning experiences with an affordable data glove.
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Min, S. et al. (2007). Simple Glove-Based Korean Finger Spelling Recognition System. In: Gervasi, O., Gavrilova, M.L. (eds) Computational Science and Its Applications – ICCSA 2007. ICCSA 2007. Lecture Notes in Computer Science, vol 4705. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74472-6_88
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DOI: https://doi.org/10.1007/978-3-540-74472-6_88
Publisher Name: Springer, Berlin, Heidelberg
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