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An Intelligent Telugu Handwritten Character Recognition Using Multi-Objective Mayfly Optimization with Deep Learning–Based DenseNet Model

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Published:10 March 2023Publication History
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Abstract

The handwritten character recognition process has gained significant attention among research communities due to its application in assistive technologies for visually impaired people, human–robot interaction, automated registry for business documents, and so on. Handwritten character recognition of the Telugu language is difficult owing to the absence of a massive dataset and a trained convolutional neural network (CNN). This article introduces an intelligent Telugu character recognition process using a multi-objective mayfly optimization with deep learning (MOMFO-DL) model. The proposed MOMFO-DL technique involves the DenseNet-169 model as a feature extractor to generate a useful set of feature vectors. A functional link neural network (FLNN) is used as a classification model to recognize and classify the printer characters. The design of the MOMFO technique for the parameter optimization of the DenseNet model and FLNN model shows the novelty of the work. The use of MOMFO technique helps to optimally tune the parameters in such a way that the overall performance can be improved. The extensive experimental analysis takes place on benchmark datasets and the outcomes are examined with respect to different measures. The experimental results pointed out the supremacy of the MOMFO technique over the recent state-of-the-art methods.

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  1. An Intelligent Telugu Handwritten Character Recognition Using Multi-Objective Mayfly Optimization with Deep Learning–Based DenseNet Model

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    • Published in

      cover image ACM Transactions on Asian and Low-Resource Language Information Processing
      ACM Transactions on Asian and Low-Resource Language Information Processing  Volume 22, Issue 3
      March 2023
      570 pages
      ISSN:2375-4699
      EISSN:2375-4702
      DOI:10.1145/3579816
      Issue’s Table of Contents

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 10 March 2023
      • Online AM: 15 March 2022
      • Accepted: 27 September 2021
      • Received: 5 August 2021
      Published in tallip Volume 22, Issue 3

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