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 Sinha, Ayushi; Gunavathi, R.; Johnsan, Amala Title A name given to the resource Deep Learning-Based Dynamic Vision: Classifying Hand Gestures 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 Lecture Notes in Networks and Systems;Volume;1182;pp.513-526 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-97-8865-1_42" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-97-8865-1_42</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105005255393?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105005255393?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 Sinha A., Christ Deemed to Be University, Pune, India; Gunavathi R., Christ Deemed to Be University, Pune, India; Johnsan A., Christ Deemed to Be University, Pune, India Description An account of the resource In the field of hand gesture recognition, this research introduces novel approaches by utilising a variety of state-of-the-art deep learning models, including YOLOv6, YOLOv8, VGG16, VGG19, and ResNet50. Our work involved rigorous dataset annotation and preprocessing, coupled with custom data augmentation techniques tailored for real-world scenarios. The results were excellent, as YOLOv6 exhibited remarkable precision, achieving an impressive Average Precision (AP) of 97.4% and recall (AR) of 90%. Meanwhile, YOLOv8s prowess shone in specific classes, where it attained a remarkable mean Average Precision (mAP) of 89%. We further explored the capabilities of classical Convolutional Neural Networks (CNNs) such as VGG16 and VGG19. These models demonstrated solid performance with an average accuracy of 74 and 67%, respectively. Our study also explored the utilization of ResNet50, which, despite its popularity in other computer vision tasks, showed a lower accuracy of 33% in the context of hand gesture recognition. This research showcases a significant leap beyond the conventional CNN-based research in hand gesture recognition, as we integrated both object detection and image classification models into the evaluation framework. Looking ahead, our research opens doors to exploring ensemble models that synergize the strengths of YOLOv6, YOLOv8, VGG16, and VGG19, promising a harmonized performance across all classes. Moreover, we advocate for further research into transfer learning techniques, anticipating even higher accuracy levels in scenarios constrained by limited training data. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource CNN; Computer vision; Deep learning; Gesture recognition; YOLOv6; YOLOv8 Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981978864-4; 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