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• W4367591330 abstract "The primary source of communication used by deaf-mute people is sign language. The task of automatically recognizing sign language motions is difficult. For the recognition of Indian sign language, a model based on hand anatomy and neural networks is proposed in this research. Features are extracted through Features from the Accelerated Segment Test (FAST) and Scale Invariant Feature Transform (SIFT) techniques, and hand anatomy is utilized for the selection of hand landmarks out of these detected points. Finally, training and testing of the model have been done by the neural network. Experimentation was performed on challenging and publicly available datasets for isolated single-letter gestures (alphabets and numbers) and word gestures (various real-life gestures) with different backgrounds and lighting conditions. Experimental results prove that fast and highly accurate results are achieved by the proposed model when compared with other available models. An accuracy of 98.90% has been achieved for isolated gestures and 97.58% for word gestures." @default.
• W4367591330 created "2023-05-02" @default.
• W4367591330 creator A5041065721 @default.
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• W4367591330 date "2023-01-01" @default.
• W4367591330 modified "2023-10-01" @default.
• W4367591330 title "Hand Anatomy and Neural Network Based Recognition of Isolated and Real-Life Words of Indian Sign Language" @default.
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• W4367591330 doi "https://doi.org/10.1007/978-3-031-31164-2_17" @default.
• W4367591330 hasPublicationYear "2023" @default.
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