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 Article Faculty Publications -Articles 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 Kavitha, K.P.; Kirubanand, V.B. Title A name given to the resource Performance Evaluation of Transfer Learning VGG16 in Handwritten Text Using Word Beam Search and Language 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 Journal of Internet Services and Information Security;Volume;15;Issue;2;pp.536-551 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.58346/JISIS.2025.I2.037" target="_blank" rel="noreferrer noopener">https://doi.org/10.58346/JISIS.2025.I2.037</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105012537403?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105012537403?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 Kavitha K.P., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India; Kirubanand V.B., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India Description An account of the resource This study evaluates the performance of transfer learning using the VGG16 model for handwritten text recognition, integrating Word Beam Search decoding and language modeling techniques. The VGG16 model, pre-trained on large-scale datasets, serves as a feature extractor for handwritten text images, capturing intricate patterns and structures inherent in handwriting. To convert these visual features into textual information, the system employs a Recurrent Neural Network (RNN) trained with the Connectionist Temporal Classification (CTC) loss function, producing a matrix of character probabilities for each time-step. The Word Beam Search algorithm is utilized for decoding these probabilities into coherent text, effectively constructing recognized text by referencing a predefined dictionary and addressing challenges such as arbitrary character strings and varying handwriting styles. The integration of language models incorporates context which further sharpens the output and improves precision and trustworthiness of recognition systems. Experimental results demonstrate that this combined approach significantly improves recognition performance, highlighting the efficacy of transfer learning and advanced decoding strategies in handwritten text recognition. This involves analyzing its effectiveness across various datasets. Transfer learning leverages pre-trained models, like VGG16, to address challenges such as limited labeled data and extensive training times. 2025, Innovative Information Science and Technology Research Group. All rights reserved. Subject The topic of the resource CNN; CTC; FSM; HTR; LM; NN; RNN; VGG Publisher An entity responsible for making the resource available Innovative Information Science and Technology Research Group Relation A related resource ISSN: 21822069; Language A language of the resource English Type The nature or genre of the resource Article 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