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 Sukruth, S.; Nickolas, S.; Jayapriya, J.; Vinay, M. Title A name given to the resource Bridging Signs to Sentences: Enhancing Sign Language Interpretation Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1542 LNNS;pp.39-52 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-9682-6_4" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-9682-6_4</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105028319078?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105028319078?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 Sukruth S., CHRIST Deemed to be University, Bengaluru, India; Nickolas S., National Institute of Technology, Tiruchirappalli, India; Jayapriya J., CHRIST Deemed to be University, Bengaluru, India; Vinay M., CHRIST Deemed to be University, Bengaluru, India Description An account of the resource This paper mainly focuses on bridging the gap between sign language recognition (SLR) and sentence formation by integrating the recognition of different signs or machine learning models with large language models (LLMs), which results in contributing and enhancing the communication of the deaf community. There are around 466million deaf individuals worldwide, where they primarily rely on Sign language for communication. Current SLR technologies have certain limitations that deal with difficulty in sentence formation and high processing requirements. This papers dataset consists of 36 classes, where 26 of them are alphabets, and the rest 9 are numbers, each consisting of 500 images. Therefore, a total of 18,000 images are present in the dataset for accurate prediction of sign languages. MediaPipe, developed by Google, is used as a tool for feature extraction by identifying 21 hand landmarks. The features extracted are then passed onto an ML model (like a Random Forest), and then the result of this model is passed on to an LLM (here, Groqs Gemma-7b-it) that forms a sentence based on the predictions. The ML model achieved a high accuracy of 99.54% and the LLMs achieved an accuracy of 93.75%. With the combination of sign language recognition and advanced modeling, this work helps to bridge signs to sentences, providing to the deaf community. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. Subject The topic of the resource Image feature extraction; Large language model (LLM); MediaPipe hand tracking; Real-time sign language translation; Sentence formation from sign language; Sign language recognition (SLR) Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981969681-9; 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