Open AccessAn Intelligent Telugu Handwritten Character Recognition Using Multi-Objective Mayfly Optimization with Deep Learning–Based DenseNet Model
Author(s) -
Vijaya Krishna Sonthi,
Srikantan S. Nagarajan,
N. Krishnaraj
Publication year - 2023
Publication title -
acm transactions on asian and low-resource language information processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.239
H-Index - 14
eISSN - 2375-4702
pISSN - 2375-4699
DOI - 10.1145/3520439
Subject(s) - computer science , telugu , artificial intelligence , convolutional neural network , benchmark (surveying) , feature (linguistics) , process (computing) , pattern recognition (psychology) , character (mathematics) , artificial neural network , deep learning , machine learning , speech recognition , linguistics , philosophy , geodesy , geography , geometry , mathematics , operating system
Handwritten character recognition process has gained significant attention among research communities due to the application in assistive technologies for visually impaired people, human robot interaction, automated registry for business document, and so on. Handwritten character recognition of Telugu language is hard owing to the absence of massive dataset and trained convolution neural network (CNN). Therefore, this paper introduces an intelligent Telugu character recognition using multi-objective mayfly optimization with deep learning (MOMFO-DL) model. The proposed MOMFO-DL technique involves DenseNet-169 model as a feature extractor to generate a useful set of feature vectors. Moreover, functional link neural network (FLNN) is used as a classification model to recognize and classify the printer characters. The design of MOMFO technique for the parameter optimization of 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 art methods.