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 Localizing and Classifying Kannada Texts Using a YOLO-Based Approach 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 Communications in Computer and Information Science;Volume;2490 CCIS;pp.177-190 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-031-90580-3_14" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-031-90580-3_14</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105015048819?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105015048819?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., Department of Computer Science, Christ University, Bengaluru, India Description An account of the resource Extracting handwritten characters from the scanned documents is a critical step due to the inherent complexities of various writing styles, inconsistent alignments, multi-touch scenarios, and overwriting characters. Expanding upon the real-time object detection capabilities of YOLOv8 (You Only Look Once), the current paper presents an experiment utilizing a dataset of 2000 handwritten images. This dataset combines the standard dataset (Chars74K) with the custom dataset featuring multi-touch handwritten text, encompassing both individual characters and character combinations that form words. The annotations were created using the Roboflow application and exported to a yaml (yet another markup language) file. The hybrid dataset was split into training, validation, and testing sets. The evaluation process yielded an accuracy of 96.8% at a threshold of 0.5 for recognizing and classifying the characters. The result suggests a positive correlation between training dataset size and model accuracy. Further, fine-tuning the hyperparameters could increase the accuracy upto 98.4%. Additional experiments were conducted to compare YOLOv8 and Detectron2 with Faster R-CNN. The results demonstrated that YOLOv8 offers substantially faster inference times, while Detectron2 with Faster R-CNN exhibited marginally higher accuracy in few classes. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. Subject The topic of the resource classification; handwritten characters; recognition; YOLO Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 18650929; ISBN: 978-303190579-7; 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