Abstract
Sign language, which is a highly visual-spatial, linguistically complete and natural language, is the main mode of communication among deaf people. In this paper, an American Sign Language (ASL) word recognition system is being developed using artificial neural networks (ANN) to translate the ASL words into English. The system uses a sensory glove CybergloveTMand a Flock of Birds® 3- D motion tracker to extract the gesture features. The finger joint angle data obtained from strain gauges in the sensory glove define the hand-shape while the data from the tracker describe the trajectory of hand movement. The trajectory of hand is normalized for increase of the signer position flexibility. The data from these devices are processed by two neural networks, a velocity network and a word recognition network. The velocity network uses hand speed to determine the duration of words. To convey the meaning of a sign, signs are defined by feature vectors such as hand shape, hand location, orientation, movement, bounding box, and distance. The second network is used as a classifier to convert ASL signs into words based on features. We trained and tested our ANN model for 60 ASL words for different number of samples. Our test results show that the accuracy of recognition is 92% .
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References
Sternberg, M.L.A.: American sign language dictionary (Revised Edition). Harper Perennial (1994) ISBN: 0-06-273275-7
Wysoski, S.G., Lamar, M.V., Kuroyanagi, S., Iwata, A.: A rotation invariant approach on static gesture recognition using boundary histograms and neural networks. In: Proceeding of the 9th International conference on neural information processing, vol. 4, pp. 2137–2141 (2002)
Vogler, C., Metaxas, D.: ASL Recognition based on a coupling between HMMs and 3D motion analysis. In: Proceedings of sixth IEEE International Computer Vision Conference, January 1998, pp. 363–369 (1998)
Lalit, G., Suei, M.: Gesture-based interaction and communication: automated classification of hand gesture contours. IEEE Transactions on Systems, Man, and Cybernetics part c: Application and Reviews 31(1), 114–120 (2001)
Yang, M.H., Ahuja, N.: Recognizing hand gestures using motion trajectories. In: IEEE, pp. 466–472 (1999)
Stanner, T., Pentland, A.: Real-time American sign language recognition from video using hidden markov models. In: Computer Vision International Symposium Proceeding, pp. 265–270 (1995)
Vogler, C., Metaxas, D.: Adapting hidden markov models for ASL recognition by using three-dimensional computer vision methods. In: Proceeding of the IEEE International Conference on Systems, Man and Cybernetics, Orlando, pp. 12–15 (1997)
Cui, Y., Weng, J.: A learning-based prediction and verification segmentation scheme for hand sign image sequence. IEEE transactions on Pattern Analysis and Machine Intelligence 21(8), 798–804 (1999)
Vogler, C., Metaxas, D.: Parallel hidden markov models for American sign language recognition. In: IEEE proceeding of International Computer Vision Conference, September 1999, pp. 116–122 (1999)
Allen, M.J., Asselin, P.K., Foulds, R.: American Sign Language Finger Spelling Recognition System. In: 29th Bioengineering Conference proceeding, IEEE, pp. 22–23 (2003)
De Marco, R.M., Foulds, R.A.: Data Recording and Analysis of American Sign Language. In: IEEE 29th proceeding of Bioengineering Conference, pp. 49–50 (2003)
Wang, H.G., Sarawate, N.N., Leu, M.C.: Recognition of American sign language gestures with a sensory glove. In: Japan USA Symposium on Flexible Automation, Denver, CO (July 2004)
Lee, L.K., Kim, S., Choi, Y.K., Lee, M.H.: Recognition of hand gesture to human-computer interaction. IEEE, 2177–2122 (2000)
Oz, C., Sarawate, N.N., Leu, M.C.: American sign language word recognition with a sensory glove using artificial neural networks. Intelligent Engineering Systems through Artificial Neural Networks 14, 633–638 (2004) ISBN 0-7918-0228-0
Wilbur, R.B.: American sign language linguistic and applied dimensions, 2nd edn. College-Hill Press (1987) ISBN 0-316-94013-5
Battison, R.: Lexical borrowing in American sign language. Linstok Pr., Silver Spring (1978) ISBN: 0-932130-02
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Oz, C., Leu, M.C. (2005). Linguistic Properties Based on American Sign Language Recognition with Artificial Neural Networks Using a Sensory Glove and Motion Tracker. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_147
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DOI: https://doi.org/10.1007/11494669_147
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