# Sign Language Sentence Recognition Using Hybrid Graph Embedding and Adaptive Convolutional Networks
## 2 Literature Survey  
[PERSON] and [PERSON] [16] presented that as COVID-19 disrupts clinical services nationwide, deaf patients struggle to communicate for care. The study demonstrates the way automatic sign language identification helps Indian deaf individuals and healthcare workers interact. A new dataset of dynamic hand gestures for ISL words averages 83.36% accuracy. The model achieved 97% accuracy on an alternative ISL dataset and 99.34 \(\pm\) 0.66% on a benchmarking hand gesture dataset. Yet, SLR accuracy issues remain when handling various gesture data, detecting and translating continuous gesture sequences, and recognizing sign languages with hands and body motions. [PERSON] et al. [17] suggested MSeqGraph, a graph-based multimodal sequential embedding network that uses implicit temporal cues and multimodal complementarity for sign language translation (SLT). The MSeqGraph model incorporates a GEU that combines channel-wise and temporal-wise, as well as multimodal, relational embedding. The GEU incorporates cross-modal edges, temporal neighborhood edges, and parallel convolutions as part of the GCN computation. Dense multimodal cues are used using a hierarchical GEU stacker. Using the USTC-CSL as well as BOSTON-104 datasets, experiments show that the suggested strategy works and that representation and performance are improved using multimodal cues. [PERSON] and [PERSON] [18] provided that deaf and hard-of-hearing people use sign language (SL), although it might be difficult with strangers. Space-temporal multi-cue (STMC) networks from deep learning improve SL prediction. These networks take longer to preprocess and annotate crucial locations. This study presents a spatiotemporal hybrid cue network (STHCN) using a Dynamic Dense Spatiotemporal Graph Convolutional Neural Network (DDSTGCNN) and feature extractor network to address ISL recognition and translation tasks for sequence learning and inference, and BLSTM encoders, CTCs, and self-attention-based LSTM decoders use extracted features. The STHCN model detects ISL 93.65% accurately. [PERSON] et al. 's study [19], given that the deaf community relies heavily on sign language translation (SLT), employs sequence-to-sequence learning. On the other hand, sign languages use a variety of visual and manual cues to communicate. The hierarchical spatiotemporal graph neural network (HST-GNN) is a new architecture for deep learning proposed in this paper to learn such graph patterns. The network characterizes local and global graph attributes using graph convolutions and graph self-attentions with the neighborhood context. Proving the efficacy of the suggested HSTGNN, experiments were conducted on benchmark datasets such as PHOENIX-2014-T and CSL. [PERSON] et al. [20] debuted an isolated word sign language recognition model built on a hybrid SKResNet-TCN network. The model reduces memory and processing requirements using grouped convolution, causal convolution, and inflation convolution. Subsequently, it outperforms conventional 3D convolutional networks in terms of accuracy, using fewer parameters and requiring easier procedures. The following area of study will be continuous sign language, but the study acknowledges that there may be discrepancies between the translated results and the actual order of speech. [PERSON] et al. [21] introduced Convolutional Neural Networks (CNN) and 1D convolutional layers, and Long Short-Term Memory (LSTM) networks were utilized in the development of a novel approach to the recognition of sign language. This technique offers a precise classification of sign language motions, allowing for the identification of temporal patterns and the management of long-term interdependence. Expanding recognition skills to incorporate other sign languages and enabling user customization are two future topics that have been pursued to promote accessibility and inclusivity for people with hearing problems. [PERSON] et al. [22] showed that Saudi Arabia only employs hearing-impaired sign language interpreters.