Automated Telugu Printed and Handwritten Character Recognition in Single Image using Aquila Optimizer based Deep Learning Model

Sonthi, Vijaya Krishna; Nagarajan, Srikantan S.; Krishnaraj, N.

doi:10.14569/ijacsa.2021.0121275

Cited by 7 publications

(5 citation statements)

References 19 publications

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“…For the purpose of automating the recognition of printed and handwritten Telegu characters inside a single image, Sonthi et al [ 57 ] suggested an AO based deep learning model. To prepare the images for analysis, an adaptive fuzzy filtering method was used and the character segmentation techniques to isolate useful areas.…”

Section: Related Work On Classical Ao and Its Improved Variantsmentioning

confidence: 99%

A Comprehensive Survey on Aquila Optimizer

Sasmal

Hussien

Das

et al. 2023

Arch Computat Methods Eng

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Aquila Optimizer (AO) is a well-known nature-inspired optimization algorithm (NIOA) that was created in 2021 based on the prey grabbing behavior of Aquila. AO is a population-based NIOA that has demonstrated its effectiveness in the field of complex and nonlinear optimization in a short period of time. As a result, the purpose of this study is to provide an updated survey on the topic. This survey accurately reports on the designed enhanced AO variations and their applications. In order to properly assess AO, a rigorous comparison between AO and its peer NIOAs is conducted over mathematical benchmark functions. The experimental results show the AO provides competitive outcomes.

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Section: Related Work On Classical Ao and Its Improved Variantsmentioning

confidence: 99%

A Comprehensive Survey on Aquila Optimizer

Sasmal

Hussien

Das

et al. 2023

Arch Computat Methods Eng

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show abstract

“…Aquila optimization using the adaptive fuzzy filtering [80] and multi-objective mayfly optimization with deep learning (MOMFO-DL) [81] techniques aims to detect and recognize handwritten Telugu characters. Character recognition is performed in the presence of both handwritten and printed text that coexist in the same document.…”

Section: Text Recognitionmentioning

confidence: 99%

A Survey on Multi-Objective Based Parameter Optimization for Deep Learning

Chakraborty,

Pal,

Bandyopadhyay

et al. 2023

csci

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Deep learning models form one of the most powerful machine learning models for the extraction of important features. Most of the designs of deep neural models, i.e., the initialization of parameters, are still manually tuned. Hence, obtaining a model with high performance is exceedingly time-consuming and occasionally impossible. Optimizing the parameters of the deep networks, therefore, requires improved optimization algorithms with high convergence rates. The single objective-based optimization methods generally used are mostly time-consuming and do not guarantee optimum performance in allcases. Mathematical optimization problems containing multiple objective functions that must be optimized simultaneously fall under the category of multi-objective optimization sometimes referred to as Pareto optimization. Multi-objective optimization problems form one of the alternatives yet useful options for parameter optimization. However, this domain is a bit less explored. In this survey, we focus on exploring the effectiveness of multi-objective optimization strategies for parameter optimization in conjunction with deep neural networks. The case studies used in this study focus on how the twomethods are combined to provide valuable insights into the generation of predictions and analysis in multiple applications.

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“…Individuals exhibit diverse writing styles, making it difficult to create a universal model that accurately recognizes all variations. Factors such as the size, slant, and spacing of characters further contribute to the complexity of the recognition process [47].…”

Section: Challenges Of Handwriting Recognitionmentioning

confidence: 99%

Machine Learning Methods for Handwriting Recognition

Peng

2023

Preprint

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Machine learning is a fundamental aspect of artificial intelligence that involves the development of algorithms and models that allow computers to learn and make predictions or decisions without explicit programming. With the development of neural networks, back-propagation algorithms and deep learning, machine learning has made breakthroughs in the fields of image recognition, natural language processing and handwriting recognition using machine learning techniques. The advent of deep learning has revolutionized the field of handwriting recognition, using convolutional neural networks, recurrent neural networks, and sequence-to-sequence models to provide solutions that go beyond machine learning methods and significantly improve the accuracy and robustness of handwriting recognition systems. But challenges remain, including the need for large labelled datasets, computational resources and addressing potential biases. As research in deep learning techniques continues to drive handwriting recognition closer towards realisability, machine learning approaches remain at the forefront.

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Automated Telugu Printed and Handwritten Character Recognition in Single Image using Aquila Optimizer based Deep Learning Model

Cited by 7 publications

References 19 publications

A Comprehensive Survey on Aquila Optimizer

A Comprehensive Survey on Aquila Optimizer

A Survey on Multi-Objective Based Parameter Optimization for Deep Learning

Machine Learning Methods for Handwriting Recognition

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