A dynamic gesture recognition system for the Korean sign language (KSL)

@article{Kim1996ADG,
  title={A dynamic gesture recognition system for the Korean sign language (KSL)},
  author={Jong-Sung Kim and Won Jang and Z. Zenn Bien},
  journal={IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society},
  year={1996},
  volume={26 2},
  pages={
          354-9
        }
}
  • Jong-Sung Kim, W. Jang, Z. Bien
  • Published 1996
  • Computer Science, Medicine
  • IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society
The sign language is a method of communication for the deaf-mute. [...] Key Method A pair of data-gloves are used as the sensing device for detecting motions of hands and fingers. For efficient recognition of gestures and postures, a technique of efficient classification of motions is proposed and a fuzzy min-max neural network is adopted for on-line pattern recognition.Expand
A glove-based gesture recognition system for Vietnamese sign language
In this paper, we address a development of a glove-based gesture recognition system for Vietnamese sign language. A sensor glove is attached ten flex sensors and one accelerometer. Here, flex sensorsExpand
Continuous gesture recognition system for Korean sign language based on fuzzy logic and hidden Markov model
  • Jung-Bae Kim, Kwang-Hyun Park, W. Bang, Z. Bien
  • Computer Science
  • 2002 IEEE World Congress on Computational Intelligence. 2002 IEEE International Conference on Fuzzy Systems. FUZZ-IEEE'02. Proceedings (Cat. No.02CH37291)
  • 2002
TLDR
This work disassemble the KSL into 18 hand motion classes according to their patterns and represents the sign words as some combination of hand motions, and adopts the hidden Markov model to recognize 15 KSL sentences. Expand
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TLDR
A Japanese sign-language recognition system using acceleration sensors, position sensors and datagloves, to understand human dynamic motions and finger geometry and a robust gesture recognition comparing with a single sensor method is proposed. Expand
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A system is proposed, which recognizes Korean sign Language (KSL), which recognizes 31 Korean manual alphabets and 131 Korean signs in real-time with recognition rate 96.7% for Korean manuals and 94.3% for Koreans sign words, excluding no recognition case. Expand
IMAGE BASED SIGN LANGUAGE RECOGNITION SYSTEM FOR SINHALA SIGN LANGUAGE
TLDR
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TLDR
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A vision-based recognition system of continuous KSL is presented, which disassemble the KSL into hand motion classes according to their patterns and rejects unintentional gesture motions such as preparatory motion and meaningless movement between sign words. Expand
A Real-time Gesture Recognition System for Isolated Swedish Sign Language Signs
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This paper describes a method for automatic recognition of isolated Swedish Sign Language signs for the purpose of educational signing-based games and presents and tests a recognizer based on manual components of sign language. Expand
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The results with test images are presented, which show that the proposed Sign Language Recognition System is able to recognize images with 98.125% accuracy when trained with 320 images and tested with 160 images. Expand
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In this proposed work, a casing work is planned for examining and distinguishing the sign motion language through various modules like Noise removal using adaptive filter, segmentation using region growing algorithm and feature extraction by using an improved genetic algorithm. Expand
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References

SHOWING 1-10 OF 13 REFERENCES
Increasing manual sign recognition vocabulary through relabelling
  • M. Waldron, Soowon Kim
  • Computer Science
  • Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)
  • 1994
TLDR
The results showed that the overall recognition rate of the relabelled network was 84% as compared to 86% for the retrained network and it was found that the dynamic sampling of the signs made the movement phoneme module unnecessary. Expand
Glove-Talk: a neural network interface between a data-glove and a speech synthesizer
TLDR
To illustrate the potential of multilayer neural networks for adaptive interfaces, a VPL Data-Glove connected to a DECtalk speech synthesizer via five neural networks was used to implement a hand-gesture to speech system, demonstrating that neural networks can be used to develop the complex mappings required in a high bandwidth interface that adapts to the individual user. Expand
A classifier using fuzzy rules extracted directly from numerical data
  • S. Abe, M. Lan
  • Mathematics
  • [Proceedings 1993] Second IEEE International Conference on Fuzzy Systems
  • 1993
The authors consider the extraction of fuzzy rules directly from numerical data for pattern classification. The fuzzy rules with variable fuzzy regions are defined by activation hyperboxes which showExpand
Fuzzy models for pattern recognition
TLDR
The basic structure of fuzzy sets theory as it applies to the major problems encountered in the design of a pattern recognition system is described. Expand
Fuzzy min-max neural networks. I. Classification
  • P. K. Simpson
  • Mathematics, Computer Science
  • IEEE Trans. Neural Networks
  • 1992
TLDR
The fuzzy min-max classifier neural network implementation is explained, the learning and recall algorithms are outlined, and several examples of operation demonstrate the strong qualities of this new neural network classifier. Expand
Fuzzy Min-Max Neural Networks-Part 1 : Classification
A supervised learning neural network classifier that utilizes fuzzy sets as pattern classes is described. Each fuzzy set is an aggregate (union) of fuzzy set hyperboxes. A fuzzy set hyperbox is anExpand
Pattern Recognition with Fuzzy Objective Function Algorithms
  • J. Bezdek
  • Computer Science
  • Advanced Applications in Pattern Recognition
  • 1981
TLDR
Books, as a source that may involve the facts, opinion, literature, religion, and many others are the great friends to join with, becomes what you need to get. Expand
Fuzzy min-max neural networks - Part 2: Clustering
TLDR
This paper will provide some background concerning the development of the fuzzy min-max clustering neural network and provide a comparison with similar work that has recently emerged and a brief description of fuzzy sets, pattern clustering, and their synergistic combination is presented. Expand
On the "Identification and control of dynamical systems using neural networks"
Referring to the above said paper by Narendra-Parthasarathy (ibid., vol.1, p4-27 (1990)), it is noted that the given Example 2 (p.15) has a third equilibrium state corresponding to the point (0.5,Expand
Data-Glove Model 2+ Operation Manual
  • Data-Glove Model 2+ Operation Manual
  • 1992
...
1
2
...