A Neural Network for the Russian Sign Language Recognition

Abstract

we implemented a neural network to recognize Russian finger-spelling gestures and described the training process. We developed image-processing procedures for gesture recognition. We used video frames as input vectors for the neural network. The network recognizes Russian finger-spelling. The system provides a scoring method to assess the skills of students learning sign language and motivates them to continue training. We described the mathematical models used to evaluate results and the mechanisms for user interaction. The study aims to improve the accessibility and effectiveness of Russian sign language instruction. The system improves the learning process for deaf and hard-of-hearing users by providing automatic gesture recognition, game-based motivation, and a clear assessment of results.