Isolated Video-Based Sign Language Recognition Using a Hybrid CNN-LSTM Framework Based on Attention Mechanism

Abstract

Sign language is a complex language that uses hand gestures, body movements, and facial expressions and is majorly used by the deaf community. Sign language recognition (SLR) is a popular research domain as it provides an efficient and reliable solution to bridge the communication gap between people who are hard of hearing and those with good hearing. Recognizing isolated sign language words from video is a challenging research area in computer vision. This paper proposes a hybrid SLR framework that combines a convolutional neural network (CNN) and an attention-based long-short-term memory (LSTM) neural network. We used MobileNetV2 as a backbone model due to its lightweight structure, which reduces the complexity of the model architecture for deriving meaningful features from the video frame sequence. The spatial features are fed to LSTM optimized with an attention mechanism to select the significant gesture cues from the video frames and focus on salient features from the sequential data. The proposed method is evaluated on a benchmark WLASL dataset with 100 classes based on precision, recall, F1-score, and 5-fold cross-validation metrics. Our methodology acquired an average accuracy of 84.65%. The experiment results illustrate that our model performed effectively and computationally efficiently compared to other state-of-the-art methods.