# Korean Sign Language Recognition Using Transformer-Based Deep Neural Network
## 2 Related Work
The advantage of their model is that they overcome the gradient vanishing and overfitting problem, whereas their model produces long-distance dependency problems in the future feature network [54]. [PERSON] et al. proposed a deep bi-direction LSTM (BiLSTM) to recognize Arabic sign language using a self-organizing map for hand shape feature extraction [55]. The same 2 DCNN, 3 DCNN, and LSTM models were used to classify skeleton-based sign language recognition [56]. More recently, researchers used different existing CNN architectures for solving specific problems, such as CNN with attention [57; 58; 59], AlexNet [21; 60] and VGGNet [61], which focus on the convolution and pooling layer, multiple paths of the basic block effectiveness showed by GoogleNet [62] and InceptionNet [63]. ResNet [22] showed better generalization by adding shortcut connections every two layers to the base network. Researchers used an attention module to overcome the existing problem as an operator between adaptable modalities [64]. Stack attention modules used as an intermediate stage between residual networks [65], SENet [24], and GENet [66] were used to analyze the interdependencies among the channels. NLNet employs neural networks as a self-attention mechanism for providing parts interconnection among the spatial position to argue the long-range dependencies [67]. Moreover, MobileNets [68] and EfficientNets [69] were recently used to provide mobile-size portable networks. Transformers are used to achieve remarkably advanced success in natural language processing tasks, and many researchers employed them as effective articles for vision tasks [21; 22; 23; 24; 26; 27]. The main transformer architecture ViT [30] is directly inherited from NLP to the computer vision domain to recognize sign language classification, although it needs a large-scale dataset to achieve satisfactory performance accuracy [70]. To overcome the large dataset problem, another researcher proposed DeiT to introduce a new training procedure with higher efficiency [33]. Another researcher proposed another extended transformer, T2T-ViT [71], to convert recursively neighboring token aggregation into single tokens. Vit used only the patch sequence, which may cause the loss of some potential information. By including pixel-level information with the patch level, another researcher proposed a TNT transformer with inner and outer blocks, respectively. PVT, CPVT, and CvT combined the transformer and CNN to overcome the long-range dependency problem, but their combination utilization was not satisfactory [72; 73; 74; 75]. Another researcher employed the CMT method to solve the combination inefficiency problem, which included four stages, a transformer, and the CNN [28]. The main drawback of the CMT is the mix-up of long-term and short dependency in each stage, which increases the computational complexity. Moreover, many transformer-based deep learning models have been developed but have not achieved satisfactory performance. We proposed a simple multi-branch transformer-based architecture to recognize sign language datasets to solve the problem. It is known that for modeling long-range dependency, transformer demonstrated higher efficiency. Moreover, in the paper, we concatenated the transformer-based feature and convolution layers feature, where the overall performance proved the efficiency of the model.