Abstract
In our day to day lives, we come across especially abled people who perform their daily chores with the aid of motivation that they get from self-confidence. There are many with hearing impairment. Sign language is the most expressed and natural way for them to communicate. Some chains of restaurants have, in fact, recruited deaf servers providing them with employment opportunities. Therefore, automatic Sign language recognition has become the crux of vision research. This paper is based on a project that builds a system that can recognize words communicated using the American Sign Language (ASL). Having been provided with a preprocessed dataset of tracked hand and nose positions extracted from the video, the set of Hidden Markov Models are trained. Using a part of this dataset, identification of individual words from test sequences is done. It provides them with the ability to communicate better, opening up a lot of opportunities.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Kim, T., Livescu, K., Shakhnarovich, G.: American sign language fingerspelling recognition with phonological feature-based tandem models (2012)
Liang, R.H., Ouhyoung, M.: A sign language recognition system using Hidden Markov model and context sensitive search
ElBadawy, M., Elons, A.S., Shedeed, H.A.: Arabic sign language recognition with 3D convolutional neural networks (2017)
Tolba, M.F., Elons, A.S.: Recent development in sign language recognition systems
Artificial intelligence, a modern approach by Stuart J Russel and Peter Norvig
Nicole, R.: Title of paper with only first word capitalized. J. Name Stand. Abbrev. (in press)
Olofsson, T.: Bayesian model selection for Markov, Hidden Markov and multinomial models (2007)
Anantha Rao, G., Syamala, K., Kishore, P.V.V.: Deep convolutional neural networks for sign language recognition (2018)
Dreuw, P., Rybach, D., Deselaers, T., Zahedi, M.: Spech recognition techniques for a sign language system. In: Interspeech (2007)
Liwicki, S., Everingham, M.: Automatic recognition of fingerspelled words in British sign language. In: CVPR (2009)
Fang, Y., et al.: A real time hand gesture recognition method. In: Proceedings International Conference on Multimedia Expo (2007)
Bowden, R., Windridge, D., Kadir, T., Zisserman, A., Brady, M.: A linguistic feature vector for the visual interpretation of sign language. In: ECCV (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Middi, V.S.R., Raju, M.A. (2021). Sign Language Recognizer Using HMMs. In: Senjyu, T., Mahalle, P.N., Perumal, T., Joshi, A. (eds) Information and Communication Technology for Intelligent Systems. ICTIS 2020. Smart Innovation, Systems and Technologies, vol 195. Springer, Singapore. https://doi.org/10.1007/978-981-15-7078-0_71
Download citation
DOI: https://doi.org/10.1007/978-981-15-7078-0_71
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-7077-3
Online ISBN: 978-981-15-7078-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)