Abstract
Hand gesture recognition has attracted the attention of many researchers due to its wide applications in robotics, games, virtual reality, sign language and human-computer interaction. Sign language is a structured form of hand gestures and the most effective communication way among hear-impaired people. Developing an efficient sign language recognition system to recognize dynamic isolated gestures encounters three major challenges, namely, hand segmentation, hand shape feature representation and gesture sequence recognition. Traditional sign language recognition methods utilize color-based hand segmentation algorithms to segment hands, hand-crafted feature extraction for hand shape representation and Hidden Markov Model (HMM) for sequence recognition. In this paper, a novel framework is proposed for signer-independent sign language recognition using multiple deep learning architectures comprising hand semantic segmentation, hand shape feature representation and deep recurrent neural network. The recently developed semantic segmentation method called DeepLabv3+ is trained using a set of pixel-labeled hand images to extract hand regions from each frame of the input video. Then, the extracted hand regions are cropped and scaled to a fixed size to alleviate hand scale variations. Extracting hand shape features is achieved using a single layer Convolutional Self-Organizing Map (CSOM) instead of relying on transfer learning of pre-trained deep convolutional neural networks. The sequence of extracted feature vectors are then recognized using deep Bi-directional Long Short-Term Memory (BiLSTM) recurrent neural network. BiLSTM network contains three BiLSTM layers, one fully connected and softmax layers. The performance of the proposed method is evaluated using a challenging Arabic sign language database containing 23 isolated words captured from three different users. Experimental results show that the performance of proposed framework outperforms with large margin the state-of-the-art methods for signer-independent testing strategy.