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. Sign language recognition is performed using various tech- nologies including vision-based [1], glove-based [2] and sensor-based [3]. The most challenging and the least accu- rate of all is the vision-based approach. This is because in vision-based approaches, there are many variances including distance from camera, tilt and movement of the camera and non-stationary backgrounds. On the other hand, gloved-based and sensor-based approaches are more accurate but are more restrictive and require setup and batteries. Additionally, sign language recognition can be applied to either sign language alphabet [4], [5], and [6], words (a.k.a. gestures) [7] and continuous sentences [8]

. It was mentioned that reducing the computational time and making the proposed solution suitable for real-time recognition of sign language remains the topic of a future work. Additional future work also include collecting and labeling a larger sentence-based dataset with more than one signer. ACKNOWLEDGMENT This work was supported in part by the Open Access Pro- gram from the American University of Sharjah under Award OAPCEN-1410-E00215. This article represents the opinions of the authors and does not mean to represent the position or opinions of the American University of Sharjah. 126832 VOLUME 11, 2023

. In SectionVII, draw the conclu- sion and future work. II. RELATED WORK Researchers employed various technologies to develop the hand gesture-based SLR system, speciﬁcally hand-crafted feature extraction with machine learning algorithms and deep learning algorithms [19], [32]. Many researchers extracted hand-crafted features and employed machine learning algo- rithms such as the Hidden Markov model (HMM),[1], Pattern Trees (SP-Tree)[33] and they reported 93.00% accuracy for Greek Sign Language (GSL) and 88.00% accuracy for Ger- man Sign Language (GSL) respectively

. Remarkably, proposed approach surpasses existing models in terms of accuracy, establishing its superiority and versatility as a more generalizable solution in sign language recognition. This positions the model as an efficacious and groundbreaking standard in the field. The presented work is organized as follows: Section II sum- marizes the existing research work and problems related to the presented work, Section III describes the bench- mark and proposed Korean sign language datasets, and Section IV describes the architecture of the proposed system—Section V demonstrated the evaluation performed VOLUME 12, 2024 34555

. In general, the two-way communication entails converting sign language into text or speech and the other way around. The latter is a simpler task, as speech recognition is a mature technology and transforming the transcribed text into sign language is a deterministic task. The associate editor coordinating the review of this manuscript and approving it for publication was Prakasam Periasamy . Sign language recognition is performed using various tech- nologies including vision-based [1], glove-based [2] and sensor-based [3]. The most challenging and the least accu- rate of all is the vision-based approach

. Some examples include American Sign Language (ASL), British Sign Language, Chinese Sign Language (CSL), and German Sign Language (GSL). Many efforts have been made to facilitate the recognition of these languages with high accu- racy using technological tools. According to an exhaustive study that reviewed 25 years of Sign Language Recognition (SLR), these efforts range from traditional approaches, such as Hidden Markov Models (HMM) and graph modeling, and deep learning approaches, such as Convolutional Neural Networks (CNN) and Transformers[2]. Moreover, acquir- ing the data for SLR tasks can be done using two main approaches

. IEEE/CVF Int. Conf. Comput. Vis. (ICCV). QC, Canada: IEEE, Oct. 2021, pp. 11283–11292, doi: 10.1109/ICCV48922.2021.01111. [35] M. Bohcek and M. Hruz, ‘‘Sign pose-based transformer for word-level sign language recognition,’’ in Proc. IEEE/CVF Winter Conf. Appl. Comput. Vis. Workshops (WACVW). HI, USA: IEEE, Jan. 2022, pp. 182–191, doi: 10.1109/WACVW54805.2022. 00024. [36] K. Xia, W. Lu, H. Fan, and Q. Zhao, ‘‘A sign language recognition system applied to deaf-mute medical consultation,’’ Sensors, vol. 22, no. 23, p. 9107, Nov. 2022, doi: 10.3390/S22239107. [37] D. R. B. da Silva, T. M. U. de Arajo, T. G. do Rgo, M. A. C. Brando, and L. M. G

. IEEE/CVF Int. Conf. Comput. Vis. (ICCV). QC, Canada: IEEE, Oct. 2021, pp. 11283–11292, doi: 10.1109/ICCV48922.2021.01111. [35] M. Bohcek and M. Hruz, ‘‘Sign pose-based transformer for word-level sign language recognition,’’ in Proc. IEEE/CVF Winter Conf. Appl. Comput. Vis. Workshops (WACVW). HI, USA: IEEE, Jan. 2022, pp. 182–191, doi: 10.1109/WACVW54805.2022. 00024. [36] K. Xia, W. Lu, H. Fan, and Q. Zhao, ‘‘A sign language recognition system applied to deaf-mute medical consultation,’’ Sensors, vol. 22, no. 23, p. 9107, Nov. 2022, doi: 10.3390/S22239107. [37] D. R. B. da Silva, T. M. U. de Arajo, T. G. do Rgo, M. A. C. Brando, and L. M. G

Question: what are the commonly used algorithms used in the papers for sign language recognition

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