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Real-Time One-Hand Indian Sign Language Alphabets and Numbers Recognition in Live Video Using Fingertip Distance Feature

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Modeling, Simulation and Optimization

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 292))

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Abstract

Sign language is the mode of communication used by hearing and speech impaired people to communicate with each other. The majority of the normal population does not have an understanding of sign language. To link this gap, the need for an automated sign language translator. Much research has been done for Indian Sign Language Recognition (SLR). Real-time sign language recognition is the need of an hour. We need to develop the SLR, which works efficiently in a real-time situation. Our study covers the development of the real-time Indian Sign Language Recognition System for one-handed ISL alphabets and numbers. We have used a hand landmark-based hand tracking module for detecting hands in real-time live video. The Euclidean Distance between hand landmarks is used as a feature to feed multilayer perceptron neural networks. We get 80% accuracy on training the model on the dataset. The prototype is tested by deploying the model in real-time SLR in live video, and it recognizing each one-handed gesture other than the gesture of alphabet ‘V’.

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References

  1. Aloysius, N., Geetha, M.: Understanding vision-based continuous sign language recognition. Multimedia Tools Appl. 79, 22177–22209 (2020)

    Article  Google Scholar 

  2. Rathi, S., Gawande, U.: Development of full-duplex intelligent communication system for deaf and dumb people. In: 2017 7th International Conference on Cloud Computing, Data Science and Engineering-Confluence, pp. 733–738, IEEE (2017)

    Google Scholar 

  3. Das, A., Gawde, S., Suratwala, K., Kalbande, D.: Sign language recognition using deep learning on custom processed static gesture images. In: 2018 International Conference on Smart City and Emerging Technology (ICSCET), pp. 1–6, IEEE (2018)

    Google Scholar 

  4. Dutta, K.K., Bellary, S.A.S.: Machine learning techniques for Indian sign language recognition. In: 2017 International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC), pp. 333–336, IEEE (2017)

    Google Scholar 

  5. Savant, R., Ajay, A.: Indian sign language recognition system for deaf and dumb using image processing and fingerspelling: a technical review. Nat. J. Syst. Inf. Technol. 11(1), 23 (2018)

    Google Scholar 

  6. Rao, G.A., Kishore, P.V.V.: Sign language recognition system simulated for video captured with smart phone front camera. Int. J. Electr. Comp. Eng. (2088–8708) 6(5) (2016)

    Google Scholar 

  7. Chattoraj, S., Vishwakarma, K., Paul, T.: Assistive system for physically disabled people using gesture recognition. In: 2017 IEEE 2nd International Conference on Signal and Image Processing (ICSIP), pp. 60–65, IEEE (2017)

    Google Scholar 

  8. Kishore, P.V.V., Kishore, S.R.C., Prasad, M.V.D.: Conglomeration of hand shapes and texture information for recognizing gestures of Indian sign language using feed-forward neural networks. Int. J. Eng. Technol. (IJET) 5(5), 3742–3756 (2013)

    Google Scholar 

  9. Saqib, S., Kazmi, S.A.R.: Repository of static and dynamic signs. Int. J. Adv. Comput. Sci. Appl. 8, 101–105 (2017)

    Google Scholar 

  10. Savant, R., Nasriwala, J.: Indian sign language recognition system: approaches and challenges. Adv. Innov. Res. 76 (2019)

    Google Scholar 

  11. Shaik, K.B., Ganesan, P., Kalist, V., Sathish, B.S., Jenitha, J.M.M.: Comparative study of skin color detection and segmentation in HSV and YCbCr color space. Proc. Comp. Sci. 57, 41–48 (2015)

    Article  Google Scholar 

  12. Tripathi, K., Nandi, N.B.G.: Continuous Indian sign language gesture recognition and sentence formation. Proc. Comp. Sci. 54, 523–531 (2015)

    Article  Google Scholar 

  13. Reshna, S., Jayaraju, M.: Spotting and recognition of hand gesture for Indian sign language recognition system with skin segmentation and SVM. In: 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), pp. 386–390, IEEE (2017)

    Google Scholar 

  14. Rautaray, S.S., Agrawal, A.: Vision based hand gesture recognition for human computer interaction: a survey. Artif. Intell. Rev. 43(1), 1–54 (2015)

    Article  Google Scholar 

  15. Sajanraj, T.D., Beena, M.V.: Indian sign language numeral recognition using region of interest convolutional neural network. In: 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), pp. 636–640, IEEE (2018)

    Google Scholar 

  16. Kaur, G., Kaur, P.: Analysis of face recognition using YCbCr and CIElab skin color segmentation methods. Int. J. Adv. Res. Comp. Sci. 6(6) (2015)

    Google Scholar 

  17. Phung, S.L., Bouzerdoum, A., Chai, D.: A novel skin color model in YCbCr color space and its application to human face detection. In: Proceedings International Conference on Image Processing, vol. 1, pp. I-I, IEEE (2002)

    Google Scholar 

  18. Sikandar, T., Ghazali, K.H., Mohd, I.I., Rabbi, M.F.: Skin color pixel classification for face detection with hijab and niqab. In: Proceedings of the International Conference on Imaging, Signal Processing and Communication, pp. 1–4 (2017)

    Google Scholar 

  19. Masood, S., Srivastava, A., Thuwal, H.C., Ahmad, M.: Real-time sign language gesture (word) recognition from video sequences using CNN and RNN. In: Intelligent Engineering Informatics, pp. 623–632. Springer, Singapore (2018)

    Google Scholar 

  20. Ahire, P.G., Tilekar, K.B., Jawake, T.A., Warale, P.B.: Two way communicator between deaf and dumb people and normal people. In: 2015 International Conference on Computing Communication Control and Automation, pp. 641–644, IEEE (2015)

    Google Scholar 

  21. Garcia, B., Viesca, S.A.: Real-time American sign language recognition with convolutional neural networks. Convolution. Neural Netw. Vis. Recogn. 2, 225–232 (2016)

    Google Scholar 

  22. Hsieh, C.C., Liou, D.H.: Novel Haar features for real-time hand gesture recognition using SVM. J. Real-Time Image Proc. 10(2), 357–370 (2015)

    Article  Google Scholar 

  23. Köpüklü, O., Gunduz, A., Kose, N., Rigoll, G.: Real-time hand gesture detection and classification using convolutional neural networks. In: 2019 14th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2019), pp. 1–8, IEEE (2019)

    Google Scholar 

  24. Nagarajan, S., Subashini, T.S.: Static hand gesture recognition for sign language alphabets using edge oriented histogram and multi class SVM. Int. J. Comp. Appl. 82(4) (2013)

    Google Scholar 

  25. Zhang, F., Bazarevsky, V., Vakunov, A., Tkachenka, A., Sung, G., Chang, C.L., Grundmann, M.: Mediapipe hands: on-device real-time hand tracking. arXiv preprint arXiv:2006.10214 (2020)

  26. “MediaPipe Hands.” https://google.github.io/mediapipe/solutions/hands

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Authors and Affiliations

  1. Faculty of Computer Science, Babu Madhav Institute of Information Technology, Uka Tarsadia University Bardoli, Bardoli, India

    Rakesh R. Savant, Jitendra V. Nasriwala & Preeti P. Bhatt

Authors
  1. Rakesh R. Savant
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  2. Jitendra V. Nasriwala
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  3. Preeti P. Bhatt
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Corresponding author

Correspondence to Rakesh R. Savant .

Editor information

Editors and Affiliations

  1. Department of Mechanical Engineering, National Institute of Technology Silchar, Silchar, India

    Biplab Das

  2. Department of Computer Science and Engineering, National Institute of Technology Silchar, Silchar, India

    Ripon Patgiri

  3. National Institute of Technology Silchar, Silchar, India

    Sivaji Bandyopadhyay

  4. Department of Automatics and Applied Software, “Aurel Vlaicu” University of Arad, Arad, Romania

    Valentina Emilia Balas

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Savant, R.R., Nasriwala, J.V., Bhatt, P.P. (2022). Real-Time One-Hand Indian Sign Language Alphabets and Numbers Recognition in Live Video Using Fingertip Distance Feature. In: Das, B., Patgiri, R., Bandyopadhyay, S., Balas, V.E. (eds) Modeling, Simulation and Optimization. Smart Innovation, Systems and Technologies, vol 292. Springer, Singapore. https://doi.org/10.1007/978-981-19-0836-1_11

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