Development of a Model for Real-Time Recognition of Kazakh Sign Language Using Mediapipe and Deep Learning Methods

Technologies for automatic processing of sign language have become an urgent need for members of society with hearing and speech impairments who face inequality in the era of digital transformation. In recent years, the issue of considering sign language as a formal structure equal to natural language and adapting it to automatic systems has attracted increasing attention from researchers. To perform the task of automatically translating information from natural language into sign language, glosses, which are the textual representation of sign language, are used as an intermediate layer. For this purpose, this study proposes a new method for converting Kazakh language text, which reflects the morphological features of the Kazakh language, into sign language glosses using natural language processing techniques. In particular, a Seq2Seq architecture based on the ByT5 small model is applied. The obtained results demonstrate that the generated gloss sequences are compact and semantically rich while preserving the internal structure of sign language. The gloss sequence makes it possible to automate the work of an interpretable intermediate layer that represents sign language movements as logical units similar to written language. The transformed gloss sequence preserves the structure of sign language, reduces redundancy, and improves sentence coherence. Thus, the use of only semantically meaningful units to control sign language avatars reduces computational requirements. Short and semantically rich glosses serve as an effective resource for synthesizing hand movements in sign language.