Abstract:
Automating real-time sign language translation
through deep learning and machine learning techniques can
greatly enhance communication between the deaf community
and the wider public. This research investigates how these
technologies can change the way individuals with speech
impairments communicate. Despite advancements, developing
accurate models for recognizing both static and dynamic
gestures remains challenging due to variations in gesture speed
and length, which affect the effectiveness of the models. We
introduce a hybrid approach that merges machine learning and
deep learning methods for sign language recognition. We
provide new model for the recognition of Kazakh Sign Language
(QazSL), employing five algorithms: Support Vector Machine
(SVM), Long Short Term Memory (LSTM), Gated Recurrent
Unit (GRU), Convolutional Neural Networks (CNN) with
VGG19, ResNet-50, and YOLOv5. The models were trained on
a QazSL dataset of more than 4,400 photos. Among the assessed
models, the GRU attained the highest accuracy of 100%,
followed closely by SVM and YOLOv5 at 99.98%, VGG19 at
98.87% for dynamic dactyls, LSTM at 85%, and ResNet-50 at
78.61%. These findings illustrate the comparative efficacy of
each method in real-time gesture recognition. The results yield
significant insights for enhancing sign language recognition
systems, presenting possible advancements in accessibility and
communication for those with hearing impairments.
