Abstract:

Sign language is a primary and widely used means of communication for individuals with hearing impairments. Current sign language recognition techniques need to be improved and need further development. In this research, we present a novel deep learning architecture for achieving significant advancements in sign language recognition by recognizing isolated signs. The study utilizes the Isolated Sign Language Recognition (ISLR) dataset from 21 hard-of-hearing participants. This dataset comprises 250 isolated signs and the x, y, and z coordinates of 543 hand gestures obtained using MediaPipe Holistic Solution. With approximately 100,000 videos, this dataset presents an essential opportunity for applying deep learning methods in sign language recognition. We present the comparative results of our experiments, where we explored different batch sizes, kernel sizes, frame sizes, and different convolutional layers. We achieve an accuracy rate of 83.32% on the test set.

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