Real-Time Multilingual Sign Language Origin Classification with Temporal Motion Features

Sign language varies across regions and cultures, yet automated classification of sign language origin remains challenging due to subtle differences in signing styles. We present TempoSign, a real-time architecture for multilingual sign language origin classification using the SpreadTheSign-Ten (SP10) dataset. Our approach extracts comprehensive features including specialized hand configurations, relative coordinate systems, and temporal motion dynamics through a pipeline that leverages MediaPipe Holistic for landmark extraction. The architecture employs a hybrid CNN-BiLSTM neural network with distinctive feature processing methodology that captures both spatial patterns and temporal relationships between frames. Using robust normalization techniques and dimensionality reduction, we achieve 96.48 % average accuracy across ten sign languages (Bulgarian, German, English, Icelandic, Italian, Lithuanian, Russian, Swedish, Ukrainian, and Chinese) with 2.44× faster inference than state-of-the-art methods (2.26s vs 5.51s). Performance analysis reveals particularly high accuracy for Icelandic (99.0%) and Chinese (98.6%) sign languages, with Russian (90.0 %) having the lowest but still competitive accuracy. While achieving slightly lower accuracy than the best-performing method (2.43% gap), TempoSign's superior computational efficiency makes it highly suitable for real-time applications including live translation systems, assistive technology, and interactive educational platforms.