Abstract
This paper presents an overview of current work on the recognition of sign language and a prototype of a simple editor for a small subset of the Brazilian Sign Language, LIBRAS. Handshape based alphabetical signs, are captured by a single digital camera, processed on-line by using computational vision techniques and converted to the corresponding Latin letter. The development of such prototype employed a machine-learning technique, based on automata theory and adaptive devices. This technique represents a new approach to be used in the far more complex problem of full LIBRAS recognition. As it happens with spoken languages, sign languages are not universal. They vary a lot from country to country, and in spite of the existence of many works in American Sign Language (ASL), the automatic recognition of Brazilian Sign Language has not been extensively studied. ...
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Braffort, A.: Research on computer science and sign language: Ethical aspects. Gesture Workshop, 1–8 (2001)
Schirmer, B.: Language and literacy development in children who are deaf. Macmillan Publishing Co., New York (1994)
Schumeyer, R., Heredia, E., Barner, K.: Region of interest priority coding for sign language videoconferencing. In: IEEE First Workshop on Multimedia Signal Processing, Princeton, pp. 531–536 (1997)
Woll, B., Sutton-Spence, R., Elton, F.: Multilingualism - the global approach to sign languages. In: The Sociolinguistics of Sign Languages, Cambridge University Press, Cambridge (2001)
Martínez, A., Wilbur, R., Shay, R., Kak, A.: Purdue RVL-SLLL ASL database for automatic recognition of american sign language. In: Proc. IEEE International Conference on Multimodal Interfaces (2002)
Pistori, H., Neto, J.J.: Decision tree induction using adaptive FSA. CLEI Electronic Journal (2004) (to appear)
Erdem, U.M., Sclaroff, S.: Automatic detection of relevant head gestures in american sign language communication. In: Proceedings of the International Conference on Pattern Recognition - ICPR 2002, Québec, Canada (2002)
Freeman, W.T., Tanaka, K., Ohta, J., Kyuma, K.: Computer vision for computer games. In: 2nd International Conference on Automatic Face and Gesture Recognition, Killington, VT, USA, pp. 100–105 (1996)
Fang, G., et al.: Signer-independent continuous sign language recognition based on SRN/HMM. In: Gesture Workshop, pp. 76–85 (2001)
Bretzner, L., Laptev, I., Lindeberg, T.: Hand gesture recognition using multi-scale colour features, hierarchical models and particle filtering. In: Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition (FGR 2002), Washington, USA, pp. 423–428 (2002)
Martin, J., Devin, V., Crowley, J.: Active hand tracking. In: IEEE Third International Conference on Automatic Face and Gesture Recognition, FG 1998, Nara, Japan (1998)
Al-Jarrah, O., Halawani, A.: Recognition of gestures in arabic sign language using neuro-fuzzy systems. Artificial Intelligence 133, 117–138 (2001)
Stenger, B., Mendonça, P.S., Cipolla, R.: Model-based hand tracking using an unscented kalman filter. In: Proc. British Machine Vision Conference, Manchester, UK, vol. I, pp. 63–72 (2001)
Davis, J., Shah, M.: Recognizing hand gestures. In: Eklundh, J.-O. (ed.) ECCV 1994. LNCS, vol. 801, Springer, Heidelberg (1994)
Kapuscinski, T., Wysocki.: Hand gesture recognition for man-machine interaction. In: Proceedings of the Second International Workshop on Robot Motion and Control, Bukowy Dworek, Poland, pp. 91–96 (2001)
Freeman, W.T., Anderson, D.B., Beardsley, P.A., Dodge, C.N., Roth, M., Weissman, C.D., Yerazunis, W.S., Kage, H., Kyuma, K., Miyake, Y., Ichi Tanaka, K.: Computer vision for interactive computer graphics. IEEE Computer Graphics and Applications 18, 42–53 (1998)
Martin, J., Crowley, J.L.: An appearance-based approach to gesture recognition. In: Proc. of 9th Int. Conf. on Image Analysis and Processing, Florence, Italy (1997)
Capovilla, F.C., Raphael, W.D.: Dicionário Enciclopédico Ilustrado Trilíngue da Língua de Sinais Brasileira. Editora da Universidade de São Paulo, São Paulo, Brasil (2001)
Neto, J.J.: Adaptative rule-driven devices - general formulation anda case study. In: CIAA 2001 Sixth International Conference on Implementation and Application of Automata, Pretoria, South Africa, pp. 234–250 (2001)
Costa, E.R., Hirakawa, A.R., Neto, J.J.: An adaptive alternative for syntactic pattern recognition. In: Proceeding of 3rd International Symposium on Robotics and Automation, ISRA, Toluca, Mexico, pp. 409–413 (2002)
Ridler, T.W., Calvard, S.: Picture thresholding using an iterative selection method. IEEE transactions on Systems, Man and Cybernetics (1978)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Pistori, H., Neto, J.J. (2004). An Experiment on Handshape Sign Recognition Using Adaptive Technology: Preliminary Results. In: Bazzan, A.L.C., Labidi, S. (eds) Advances in Artificial Intelligence – SBIA 2004. SBIA 2004. Lecture Notes in Computer Science(), vol 3171. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28645-5_47
Download citation
DOI: https://doi.org/10.1007/978-3-540-28645-5_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23237-7
Online ISBN: 978-3-540-28645-5
eBook Packages: Springer Book Archive