## 2 Related Work  
Several researchers have proposed various methodologies to implement a system to recognize sign language gestures. We have categorized the literature survey into traditional, deep learning, and attention-based SLR methods. Traditional SLR methods: [PERSON] et al. extracted features based on the angle and location of the fingertips. Support vector machine (SVM) model is used to predict classes and achieved 89.70% accuracy [6]. The authors in [7] merged HOG and LBP feature descriptors for hand gesture recognition with a recognition accuracy of 92% on the National University of Singapore (NUS) dataset. In [8], the authors implemented a machine learning-based framework that applied segmentation and used discrete wavelet transform (DWT) with SURF for feature extraction. Multiclass SVM with the bag of words technique is used to classify hand gestures with an overall accuracy of 96.5%. The authors in [9] employed HOG features with two classifiers for hand gesture recognition with an accuracy of 96% and 98% with the K-nearest neighbor (KNN) and SVM, respectively. A multimodal feature vector is obtained using distance transform, contour feature extraction, and K-curvature convex defects techniques for recognizing hand gesture RGB depth dataset [10]. The system acquired a recognition accuracy of 97.4%. Deep learning based: In the paper [11], the authors used the VGG16 model to classify 36 static signs, including 26 alphabets and ten digits from the American Sign Language(ASL) database. They fine-tuned the parameters using the transfer learning technique on the VGG16 model, which is already trained on ImageNet data and comprises more than a million images. With this methodology, they acquired an impressive accuracy of 96%. [PERSON] et al. [12] proposed an Indian Sign Language (ISL) static alphabet recognition system. The face was detected using a face detection algorithm and segmented the hand region using color-based segmentation. The approach uses a deep learning algorithm to recognize 24 sign classes. The methodology obtained a validation accuracy of 98.64%. The authors have presented a system by concatenating features from two CNN streams on the ASL alphabet dataset using various deep learning models for classification. The system achieved the best accuracy of 97.57% with ResNet50 as classifier [13]. A seven-layered CNN [14] for hand gesture recognition with data augmentation and skin segmentation has attained 96.5% and 96.57% accuracy on two benchmark datasets, respectively. Attention-based: Nowadays, attention-based deep learning frameworks have shown tremendous growth in achieving more accurate recognition than other deep learning models. The features from the previous layers are fused into an attention block to extract knowledgeable features [15]. The authors in the paper [16] proposed a methodology that used a residual attention-based system that helped enhance feature strength. However, the residual attention module is not portable and computationally expensive. [PERSON] et al. [17] designed a framework applying channel and spatial attention with the bottleneck attention module (BAM). It focused on the refinement of the intermediate features of the CNN and provided better results than the other related frameworks. [PERSON] et al. [18] developed a selective kernel networks (SkNet) model to analyze the attention process in the convolution layer. It assigned weights dynamically with different scaled convolutional kernels. This approach has shown the value of receptive fields in the recognition of images. An attention-based network consisting of detail, subject, and channel attention modules for the enhancement of features was proposed and validated on two public datasets that achieved remarkable results compared to the baseline models [19]. The literature shows deep learning networks and traditional feature descriptors have achieved good gesture recognition results. However, most existing methods have focused only on the global image features.