Abstract
Sign language is a type of language that includes postures and body motions in addition to hand gestures. For ages, sign language was the only way to connect with each other. But in early times, without the knowledge of different varieties of language, it became hard to communicate. Now as the world is becoming more advanced and digitalised, deaf and blind people find the basic mode of communication more disrupting and uneasy. To resolve this issue, Sign language recognition/interpreter system becomes a necessity to help the people in need. This is possible because to Machine Learning and Human Computer Interaction (HCI).
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Wu, J., Sun, L., Jafari, R.: A wearable system for recognizing american sign language in RealTime using IMU and surface EMG sensors. IEEE J. Biomed. Heal. Informatics 20(5), 1281–1290 (2016). https://doi.org/10.1109/JBHI.2016.2598302
Ding, L., Martinez, A.M.: Modelling and recognition of the linguistic components in American sign language, ǁ Image Vis. Comput. 27(12), 1826–1844 (2009). Nov.
Kelly, D., Delannoy, R., Mc Donald, J., Markham, C.: A framework for continuous multimodal sign language recognition. In: Proc. Int. Conf. Multimodal Interfaces, Cambridge, MA, pp. 351–358 (2009)
Augustian Isaac, R., Sri Gayathri, S.: Sign Language Interpreter. IRJET 5(10) (October 2018). p-ISSN – 2395-0072
for The Deaf and Dumb. Image Vis. Comput. 27(12), 1826–1844 (Nov. 2009)
Fang, G., Gao, W., Zhao, D.: Large vocabulary sign language recognition based on fuzzy decision trees. IEEE Trans. Syst. Man Cybern. A Syst. Humans 34(3), 305–314 (May 2004)
Mukhopadhyay, M., et al.: Facial emotion recognition based on Textural pattern and Convolutional Neural Network. In: 2021 IEEE 4th International Conference on Computing, Power and Communication Technologies (GUCON), pp. 1–6 (2021). https://doi.org/10.1109/GUCON50781.2021.9573860
Sinha, T., Chowdhury, T., Shaw, R.N., Ghosh, A.: Analysis and Prediction of COVID-19 Confirmed Cases Using Deep Learning Models: A Comparative Study. In: Bianchini, M., Piuri, V., Das, S., Shaw, R.N. (eds.) Advanced Computing and Intelligent Technologies. LNNS, vol. 218, pp. 207–218. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-2164-2_18
Purva, N., Vaishali, K.: Indian Sign language Recognition: A Review. IEEE proceedings on International Conference on Electronics and Communication Systems, pp. 452–456 (2014)
Pravin, F., Rajiv, D.: HASTA MUDRA An Interpretation of Indian Sign Hand Gestures. 3rd International conference on Electronics Computer technology 2, 377–380 (2011)
Augustian Isaac, R., Sri Gayathri, S.: Sign Language Interpreter. IRJET 5(10) (October 2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Rathore, Y.S., Jain, D., Singh, P., Ahmed, W., Pandey, A.K. (2023). Sign Language Recognizing Using Machine Learning. In: Shaw, R.N., Paprzycki, M., Ghosh, A. (eds) Advanced Communication and Intelligent Systems. ICACIS 2022. Communications in Computer and Information Science, vol 1749. Springer, Cham. https://doi.org/10.1007/978-3-031-25088-0_35
Download citation
DOI: https://doi.org/10.1007/978-3-031-25088-0_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-25087-3
Online ISBN: 978-3-031-25088-0
eBook Packages: Computer ScienceComputer Science (R0)