# Korean Sign Language Recognition Using Transformer-Based Deep Neural Network
## 2 Related Work  
Sign language classification, continuous sign language recognition (SLR), and sign language translation are the main subdomains of the SLR. Many researchers have been working to develop sign language recognition systems for various languages with several machine learning and deep learning algorithms [34; 35]. As different countries have their own sign language based on their mainstream language, researchers have been working to develop a sign language recognizer for individual sign languages, among which the Bengali sign language recognizer was developed with the machine learning and deep learning algorithm [4; 5; 36; 37; 38]. [PERSON] et al. applied the hidden Markov model to classify the linguistic labeling sub-unit sign. They collected over 961 images with the Kinect TM device and performed well [39]. Additionally, the HMMs model can learn non-discriminatory features, but sometimes it ignores the data coming from the alternate class. In addition, it cannot sell useful features with it. [PERSON] et al. proposed a multi-class sign language classification model using the sequential pattern trees, namely the SP-tree boosting algorithm, where they mainly extracted hand trajectory features from the subunit of the image [40]. To evaluate their model, they used Greek sign language (GSL), which achieved 93.00% accuracy and German sign language, where they achieved 88.00% accuracy, which is better than the hidden Markov model. [PERSON] et al. proposed a phonological structure based on decomposition and feature extraction with the RGB-D sign language image. After applying a support vector machine (SVM) as a classifier, they achieved 80% accuracy [41]. [PERSON] et al. proposed an ASL recognition model using an artificial neural network (ANN) and SVM, where they achieved higher performance accuracy with ANN compared to the HMM and SVM [42]. In addition, 98.2% accuracy was achieved with SVM for the five wearable sign language devices [43], the fuzzy network was used for Chinese sign language (CSL) [44] and KNN, LDA, and SVM were applied for the ASL for 0-9, which achieved 98% higher accuracy [45]. Moreover, some other machine learning-based algorithms were employed to sign language recognition and achieve satisfactory accuracy, such as the multi-stream hidden Markov model [46], hidden conditional random field (HCRF), and random decision forest (RDF) [47; 48]. Computational complexity, the inefficiency of selecting potential images and many other drawbacks are faced by researchers in the general machine learning algorithm for image-based sign language recognition. To overcome the problems, researchers used an ANN-based algorithm for sign language recognition [49; 50]. In addition, [PERSON] et al. applied ensemble ANN to classify Korean sign language. Based on the 10 labels and 1500 samples, they achieved 97.4% accuracy by considering only finger spelling signs. [PERSON] et al. employed a model for making a dataset for KSA [51], then [PERSON] et al. employed translation modeling for exploiting 2D human pose key points and released a KSL dataset [52]. Recently, the computer vision domain has largely switched toward the deep learning-based model due to the performance and various complexity-related issues. [PERSON] et al. proposed a single and fusion parallel 3-dimensional convolutional neural network (3 DCNN) to recognize three gesture datasets where they achieved 84.38%, 34.9%, and 70% for 40, 23, and 10 class datasets, respectively, for the signer-independent and +10% for the signer-dependent cases [53]. Their performance accuracy is higher than the same previous method. [PERSON] et al. [37] proposed CNN and the long short-term memory (LSTM) algorithm based on the feature pooling module (FPM) and attention feature, where they evaluated Turkish sign language and achieved good performance. They mainly used an attention module to achieve convergence points as quickly as possible. [PERSON] et al. proposed a deep convolutional neural network (DCNN) and LSTM to recognize ASL and CSL sign language.