Abstract—Sign language recognition problem should be
represented as a time series classification model with high
accuracy. In the previous studies, Indonesian sign language
(BISINDO) had been modeled with one of stochastic time series
classification model, i.e. Hidden-Markov Model (HMM), but
has low accuracy. In other studies, BISINDO had been
recognized with high accuracy but using an unrepresentative
model (non-time series classification model), i.e. the modified
Generalized Linear Vector Quantization (mGLVQ) model with
mode function. In this paper, we tried to use a deterministic
time series classification model, named Accurate and Fast
Dynamic Time Warping (AF-DTW) model. AF-DTW model is a
modified form of Dynamic Time Warping (DTW) model. It is
not only to improve the accuracy of DTW but also to accelerate
the finding of optimal warping path. The output results showed
that AF-DTW has a much higher accuracy than HMM,
although it is not as accurate as mGLVQ.
Index Terms—BISINDO, dynamic time warping, sign language recognition, time series classification.
Tri Handhika, Dewi Putrie Lestari, Ilmiyati Sari, and Murni are with the Center for Computational Mathematics Studies, Gunadarma University, 100 Margonda Raya St., Pondok Cina, Depok, West Java, 16424, Indonesia (e-mail:firstname.lastname@example.org, email@example.com, firstname.lastname@example.org, email@example.com).
Revaldo Ilfestra Metzi Zen is with the Big Data Division, Metra Digital Media, Wisma Aldiron Dirgantara 2nd Floor Suite 202-209, South Jakarta 12780, Indonesia (e-mail: firstname.lastname@example.org).
Cite: Tri Handhika, Dewi Putrie Lestari, Ilmiyati Sari, Revaldo Ilfestra Metzi Zen, and Murni, "Indonesian Sign Language (BISINDO) Recognition Using Accurate and Fast Dynamic Time Warping Learning Model," International Journal of Machine Learning and Computing vol. 10, no. 2, pp. 381-386, 2020.Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).