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 Faculty Publications Conference Paper Faculty Publications- Conference Papers 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/. Creator An entity primarily responsible for making the resource Helen Josephine, V.L.; Rajagopal, Manikandan; Kavitha, S.; Rajendran, Stuthi Title A name given to the resource Enhancing Sign Language Recognition Through LSTM Model Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025 - Proceedings;pp.986-989 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICSADL65848.2025.10933396" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICSADL65848.2025.10933396</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105002463659?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105002463659?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Helen Josephine V.L., School of Business and Management, Christ University, Karnataka, Bangalore, India; Rajagopal M., School of Business and Management, Christ University, Karnataka, Bangalore, India; Kavitha S., Dayanand Sagar University, Bengaluru, India; Rajendran S., School of Business and Management, Christ University, Karnataka, Bangalore, India Description An account of the resource Sign language recognition is a remarkable task in this project completed through two state-of-the-art methods, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). This way, the system is able to quickly process each frame of the webcam with real-time information regarding face, body and posture in order to extract critical values. this research seeks to provide the necessary resources and opportunities for deaf people to be able to communicate effectively, obtain an education and enjoy their lives as much as other human beings This makes it a very important tool for education where the system can convert sign motions into text on-the-fly. The data was collected through a live camera, and key points from face, body, and pose were detected for training the model. Kindergarten used the four categories of vegetables, fruits, colors and animals. There were 40 video sequences of 40 frames with a sign in each. the model tries to fit too much to noisy points of data. However comprehensive the training, after 19 epochs the validation accuracy is an impressive 93%. The oscillations in the truth values of models are indicative of some uncertainty in learning where the accuracy is still to be settled. The graph in general shows that the LSTM based sign language movement classifier has a good capacity to learn and identify sign language movements with high precision. 2025 IEEE. Subject The topic of the resource Education for hearing impaired; GRU; Image/video recognition; LSTM; Machine Learning; Sign Language Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833152392-3; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online