Indonesian Language Sign System (SIBI) Recognition Using Threshold Conditional Random Fields
Abstract
The Sign System for Indonesian Language or 'Sistem Isyarat Bahasa Indonesia' (SIBI) is a sign language system that is used to represent Indonesian language. The referred sign language is a systematic movement of fingers and hands to represent a vocabulary. This paper utilizes the Threshold Conditional Random Field (TCRF) model to identify gesture and non-gesture automatically. The generated model is an early model to establish a SIBI translation system automatically. Data that were utilized in this research are Skeleton, Image, and Skeleton-Image Combination. Data were processed by implementing TCRF algorithm to provide gesture and non-gesture labels automatically. Several experiments had pointed to the highest accuracy up to 81, 5% by using skeletal data as an input in TCRF.
References
[1]
E. Rakun, "Pengenalan Komponen Imbuhan dan Kata Dasar pada Isyarat Kata Berimbuhan dalam SIBI (Sistem Isyarat Bahasa Indonesia) dengan Menggunakan Probabilistic Graphical Models," University of Indonesia, 2017
[2]
E. Rakun, M. Febrian Rachmadi, A. Tjandra, and K. Danniswara, "Spectral Domain Cross Correlation Function and Generalized Learning Vector Quantization for Recognizing and Classifying Indonesian Sign Language," in IEEE International Conference on Advanced Computer Science and Information Systems (ICACSIS), 2012, pp. 213--218.
[3]
E. Rakun, M. Adriani, I. W. Wiprayoga, K. Danniswara, and A. Tjandra, "Combining depth image and skeleton data from Kinect for recognizing words in the sign system for Indonesian language (SIBI [Sistem Isyarat Bahasa Indonesia])," in IEEE International Conference on Advanced Computer Science and Information Systems (ICACSIS), 2013, pp. 387--392.
[4]
D. Kelly, J. Mc Donald, and C. Markham, "Evaluation of Threshold Model HMMs and Conditional Random Fields for Recognition of Spatiotemporal Gestures in Sign Language," IEEE 12th Int. Conf. Comput. Vis. Work. ICCV Work. 2009, pp. 490--497, 2009.
[5]
H. Chung, H. D. Yang, "Conditional Random Field-Based Gesture Recognition with Depth Information," Optical Engineering Vol 52(1) 017201, 2013.
[6]
H. D. Yang, S. Sclaroff, and S.W. Lee, "Sign Language Spotting with a Threshold Model Based on Conditional Random Fields," IEEE Trans. Pattern Anal. Mach. Intell. 31(7), 2009, pp.1264--1277.
[7]
S. Siswomartono, Cara Mudah Belajar SIBI (Sistem Isyarat Bahasa Indonesia). Jakarta: Federasi Nasional untuk Kesejahteraan Tunarungu Indonesia, 2007.
[8]
S. S. Cho, H. D. Yang, and S. W. Lee, "Sign Language Spotting Based on Semi-Markov Conditional Random Field," 2009 Work. Appl. Comput. Vision, WACV 2009, 2009.
[9]
E. Rakun, "Pengenalan Komponen Imbuhan dan Kata Dasar pada Isyarat Kata Berimbuhan dalam SIBI (Sistem Isyarat Bahasa Indonesia) dengan Menggunakan Probabilistic Graphical Models," University of Indonesia, 2017.
Index Terms
- Indonesian Language Sign System (SIBI) Recognition Using Threshold Conditional Random Fields
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Published In
October 2019
522 pages
ISBN:9781450376570
DOI:10.1145/3373509
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- Hebei University of Technology
- Beijing University of Technology
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 25 March 2020
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- Research-article
- Research
- Refereed limited
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- Universitas Indonesia Research Grant
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ICCPR '19
ICCPR '19: 2019 8th International Conference on Computing and Pattern Recognition
October 23 - 25, 2019
Beijing, China
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