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- W3020433882 abstract "Hearing impaired people have the ability to communicate with their hands and interpret sign language (SL), but this builds a communication gap with normal people. There are models for SL recognition that have images sequence RGB as input; however, the movements of the body in 3D space is necessary to consider due to the complexity of the gestures. We built a model for Peruvian sign language recognition (PSL) to Spanish composed by 4 phases; first, the preprocessing phase in charge to process RGB, depth and skeleton streams obtained through the Kinect sensor v.1; second, the feature extraction which learn spatial information through 3 types of convolutional neural network (CNN); third, the bidirectional long short term memory (BLSTM) with residual connections in charge to reduced and encode the information. Finally, a decoder with attention mechanism and maxout network which learn the temporal information. Our proposed model is evaluated in LSA64 and ourself-built dataset. The experimental results show significant improvement compared to other models evaluated in these dataset ." @default.
- W3020433882 created "2020-05-01" @default.
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- W3020433882 date "2020-01-01" @default.
- W3020433882 modified "2023-09-27" @default.
- W3020433882 title "Peruvian Sign Language Recognition Using a Hybrid Deep Neural Network" @default.
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- W3020433882 doi "https://doi.org/10.1007/978-3-030-46140-9_16" @default.
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