Performance Analysis of Sign Language Recognition System Using Hybrid Feature Descriptor
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-19-2538-2_38
Reference16 articles.
1. Agada R, Akinlaja T, Yan J (2018) Multi sensor performance driven data fusion for sign language synthesis. In: Proceedings—2018 international conference on computational science and computational intelligence, CSCI 2018, pp 458–461. Institute of Electrical and Electronics Engineers Inc
2. Alonzo O, Glasser A, Huenerfauth M (2019) Effect of automatic sign recognition performance on the usability of video-based search interfaces for sign language dictionaries. In: ASSETS 2019—21st international ACM SIGACCESS conference on computers and accessibility, pp 56–67. Association for computing machinery, Inc
3. Bhavsar H, Trivedi J (2021) Performance comparison of svm, cnn, hmm and neuro-fuzzy approach for indian sign language recognition. Indian J Comput Sci Eng 12:1093–1101
4. Bhuiyan RA, Tushar AK, Ashiquzzaman A, Shin J, Islam MR (2018) Reduction of gesture feature dimension for improving the hand gesture recognition performance of numerical sign language. In: 20th international conference of computer and information technology, ICCIT 2017, pp 1–6. Institute of Electrical and Electronics Engineers Inc
5. Bragg D, Koller O, Caselli N, Thies W (2020) Exploring collection of sign language datasets: privacy, participation, and model performance. In: ASSETS 2020—22nd international ACM SIGACCESS conference on computers and accessibility. Association for Computing Machinery, Inc
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