Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Evaluating the Pertinence of Pose Estimation model for Sign Language Translation Subject The topic of the resource deep learning model; landmark detection; machine learning model; pose estimation; SLR models Description An account of the resource Sign Language is the natural language used by a community that is hearing impaired. It is necessary to convert this language to a commonly understandable form as it is used by a comparatively small part of society. The automatic Sign Language interpreters can convert the signs into text or audio by interpreting the hand movements and the corresponding facial expression. These two modalities work in tandem to give complete meaning to each word. In verbal communication, emotions can be conveyed by changing the tone and pitch of the voice, but in sign language, emotions are expressed using nonmanual movements that include body posture and facial muscle movements. Each such subtle moment should be considered as a feature and extracted using different models. This paper proposes three different models that can be used for varying levels of sign language. The first test was carried out using the Convex Hull-based Sign Language Recognition (SLR) finger spelling sign language, next using a Convolution Neural Network-based Sign Language Recognition (CNN-SLR) for fingerspelling sign language, and finally pose-based SLR for word-level sign language. The experiments show that the pose-based SLR model that captures features using landmark or key points has better SLR accuracy than Convex Hull and CNN-based SLR models. 2023 World Scientific Publishing Europe Ltd. Creator An entity primarily responsible for making the resource Amrutha K.; Prabu P. Source A related resource from which the described resource is derived International Journal of Computational Intelligence and Applications, Vol-22, No. 1 Publisher An entity responsible for making the resource available World Scientific Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1142/S1469026823410092" target="_blank" rel="noreferrer noopener">https://doi.org/10.1142/S1469026823410092</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85158862167&amp;doi=10.1142%2FS1469026823410092&amp;partnerID=40&amp;md5=eb20e29380b7d256fda5b1eef4a8cd57" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85158862167&amp;doi=10.1142%2fS1469026823410092&amp;partnerID=40&amp;md5=eb20e29380b7d256fda5b1eef4a8cd57</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 14690268 | CS; 2023-2024; Vol-1; 00263-00264 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Amrutha K., Department of Computer Science, CHRIST (Deemed to Be University), Bengaluru, Karnataka, India; Prabu P., Department of Computer Science, CHRIST (Deemed to Be University), Bengaluru, Karnataka, India