A WEBCAM-BASED RECOGNITION SYSTEM FOR THE UZBEK FINGERSPELLING ALPHABET

Sevinch, Jovliyeva

Innovative Multidisciplinary Journal of Applied Technology · 2026-yil

Annotatsiya

For individuals with hearing or speech impairments, sign language serves as the primary channel of natural connection with society. To date, work on the automated recognition of Uzbek sign language remains scarce, and an openly available dataset for this purpose is essentially nonexistent. This paper presents a system that recognizes the Uzbek fingerspelling alphabet using an ordinary webcam. Hand movements are tracked with the MediaPipe Hands tool; letters represented by static hand poses are classified using a Random Forest classifier, while letters expressed through motion are recognized using an LSTM neural network. Trained on a manually collected dataset of 1,523 static frames and 468 motion sequences, the system recognized static letters with 99.11% accuracy and dynamic letters with 65.96% accuracy. The results indicate that the recognition of stable hand poses can be achieved with high reliability, whereas the recognition of temporally evolving gestures remains a considerably more complex scientific and technical challenge.

Maqola ma’lumotlari
MualliflarSevinch, Jovliyeva
JurnalInnovative Multidisciplinary Journal of Applied Technology
Nashr sanasi2026-03-31
Jild4
Son3
Betlar44-48
TilIngliz
DOI10.51699/x67ey438

Kalit so‘zlar

Uzbek Sign Language, Fingerspelling Alphabet, Mediapipe, Random Forest, LSTM, Webcam

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