KAZAKH SIGN LANGUAGE RECOGNITION BY USING MACHINE LEARNING METHODS

Abstract

Pattern recognition involves identifying objects within images, where the objects are abstract and can vary in shape. We have chosen to explore the relevant area of gesture recognition. To recognize Kazakh Sign Language (KSL), it is essential first to learn the Kazakh Sign Alphabet (KSA). Training a neural network to recognize KSL requires collecting datasets in the form of images depicting hand gestures. Gesture recognition is a classification task, which is a subset of pattern recognition. The fundamental basis of recognition lies in the theory of pattern recognition.This publication focuses on the study of supervised learning methods, deep learning techniques, classification tasks, and gesture recognition trained on proprietary datasets (images obtained and divided into frames from video sequences captured by a webcam or mobile device). These methods significantly expand the range of tasks that can be effectively solved in real-time within the field of gesture recognition.The study presents results using metrics for testing deep learning models. The recognition accuracy of these models has been demonstrated based on precision, recall, F1-score, and support for each class, as well as overall accuracy and average scores. A hybrid architectural neural network model utilizing recurrent and convolutional neural network layers has been tested. Software has been developed to recognize gestures in real-time.