Sign Language Recognition Using Optimized Convolutional Neural Networks
DOI:
https://doi.org/10.61841/8sh0fk87Keywords:
Sign language, sign language recognition, convolutional neural networks, segmentation, image processing, hand masking, sign language detection, spatial features,, contour extraction, American Sign Language.Abstract
The method of communication with the people having hearing and speech impairments is based primarily on sign languages and the lack of knowledge about the various sign languages makes this communication difficult. This project focuses on developing a system where user input based of hand sign gestures will be converted to the corresponding alphabets. Some challenges associated with this field are useful feature extraction and classification of various signs, extraction of the hand boundaries and identification of signs which involve a motion of the hand since these require the extraction of temporal features. This project is focused on optimizing the 2-D convolutional neural networks for extraction of spatial features in the hand sign images for Sign Language Recognition.
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