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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

A CNN based Approach for Handwritten Character Identification of Telugu Guninthalu using Various Optimizers

Author 1: B. Soujanya
Author 2: Suresh Chittineni
Author 3: T. Sitamahalakshmi
Author 4: G. Srinivas

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2022.0130482

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 4, 2022.

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Abstract: Handwritten character recognition is the most critical and challenging area of research in image processing. A computer's ability to detect handwriting input from various original sources, such as paper documents, images, touch screens, and other online and offline devices, may be classified as this recognition. Identifying handwriting in Indian languages like Hindi, Tamil, Telugu, and Kannada has gotten less attention than in other languages like English and Asian dialects like Japanese and Chinese. Adaptive Moment Estimation (ADAM), Root Mean Square Propagation (RMSProp) and Stochastic Gradient Descent (SGD) optimization methods employed in a Convolution Neural Network (CNN) have produced good recognition, accuracy, and training and classification times for Telugu handwritten character recognition. It's possible to overcome the limitations of classic machine learning methods using CNN. We used numerous handwritten Telugu guninthalu as input to construct our own data set used in our proposed model. Comparatively, the RMSprop optimizer outperforms ADAM and SGD optimizer by 94.26%.

Keywords: Character recognition; Adam; RMSProp; SGD; CNN

B. Soujanya, Suresh Chittineni, T. Sitamahalakshmi and G. Srinivas, “A CNN based Approach for Handwritten Character Identification of Telugu Guninthalu using Various Optimizers” International Journal of Advanced Computer Science and Applications(IJACSA), 13(4), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130482

@article{Soujanya2022,
title = {A CNN based Approach for Handwritten Character Identification of Telugu Guninthalu using Various Optimizers},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130482},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130482},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {4},
author = {B. Soujanya and Suresh Chittineni and T. Sitamahalakshmi and G. Srinivas}
}


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