LAO SIGN LANGUAGE RECOGNITION USING DEEP LEARNING

The deaf and hearing-impaired community inLaos faces significant communication barriers due to the scarcity of resources and technological support for Lao Sign Language (LSL).This study addresses this gap by developing and rigorously evaluating a deep learning-based system for automated LSL recognition.We introduce a new, high-quality video dataset of 60 common LSL signs relevant to Lao culture and daily life.A comprehensive comparative analysis was conducted on four deep learning architectures: a CNN-LSTM, a CNN-LSTM with an attention mechanism (Hybrid-Attention), a Time Distributed CNN with Global Pooling (TD-CNN-Global Pool), and a 3DCNN-LSTM.All models were trained on a dataset expanded through extensive offline augmentation, using a 5-fold Group K Fold cross-validation strategy to ensure robust evaluation.The results demonstrate the critical importance of transfer learning, with models using a pre-trained MobileNetV2 backbone significantly outperforming a model trained from scratch.The standard CNN-LSTM architecture emerged as the superior model, achieving a mean cross-validation accuracy of 92.27%.The finalized model achieved an official accuracy of 93.33% on a completely unseen hold-out test set.This work establishes a strong baseline for LSL recognition, provides a valuable public dataset, and highlights the essential role of transfer learning and data augmentation in developing assistive technologies for low-resource languages.