Abstract
In recent years, for recognizing sign language, several hardware approaches have been developed using the leap motion controller and Kinect sensors. The sensor-based approaches were costly, and the complexity of designing was high. In machine learning approaches, it was found that less accuracy of prediction occurs as compared to other approaches. As a solution, machine learning-based approaches using image processing evolved as an approach with better prediction and accuracy. In this paper, deep learning approach for sign language recognition (SLR) system has been proposed using AlexNet model. AlexNet is pretrained model, and it is being tested on Indian Sign Language (ISL) Dataset pf Stanford University comprising of alphabets (A–Z) and numerals (0–9). The proposed model has yielded an accuracy of 99.6%.
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Singh, S., Bhateja, V., Srivastav, S., Pratiksha, Lin, J.CW., Travieso-Gonzalez, C.M. (2023). AlexNet Model for Sign Language Recognition. In: Bhateja, V., Yang, XS., Chun-Wei Lin, J., Das, R. (eds) Intelligent Data Engineering and Analytics. FICTA 2022. Smart Innovation, Systems and Technologies, vol 327. Springer, Singapore. https://doi.org/10.1007/978-981-19-7524-0_46
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DOI: https://doi.org/10.1007/978-981-19-7524-0_46
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