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 Malini, M.; Hemanth, K.S. Title A name given to the resource Enhancing Kannada Handwritten Text Processing: A Deep Learning Approach to Optimized Recognition and Segmentation 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 Procedia Computer Science;Volume;260;pp.439-446 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.procs.2025.03.221" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.procs.2025.03.221</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105008869372?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105008869372?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 Malini M., School of CSA, REVA University, Bengaluru, India; Hemanth K.S., Christ University, Bengaluru, India Description An account of the resource Digitalization ensures that information is available in diverse regional languages, empowering more cultures and perspectives to be heard and understood. One of the regional languages considered for empowering information access is the Handwritten Kannada document. Extracting text from these documents requires overcoming several obstacles, such as deciphering diverse handwriting styles, accommodating inconsistencies in character size, and the presence of multiple touches between characters. The present paper explored recognizing and segmenting Kannada handwritten characters using a deep learning model, specifically YOLOv8. While YOLOv8 is primarily known for real-time object detection, the paper suggests its potential for character detection tasks. The model achieved a promising mean Average Precision (mAP) of 96.8% at a threshold of 0.5 on a hybrid dataset consisting of 2476 images and 95.0% on character segmentation. This experiment adds to the growing body of research exploring YOLOv8s capabilities beyond traditional real-time object detection and instance segmentation. 2025 The Authors. Published by Elsevier B.V. Subject The topic of the resource Kannada Handwritten Characters; Recognition; Segmentation; Text Processing Publisher An entity responsible for making the resource available Elsevier B.V. Relation A related resource ISSN: 18770509; 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 All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online