Telugu letters dataset and parallel deep convolutional neural network with a SGD optimizer model for TCR

Josyula Siva Phaniram, Mukkamalla Babu Reddy


Because of the rapid growth in technology breakthroughs, including multimedia and cell phones, Telugu character recognition (TCR) has recently become a popular study area. It is still necessary to construct automated and intelligent online TCR models, even if many studies have focused on offline TCR models. The Telugu character dataset construction and validation using an Inception and ResNet-based model are presented. The collection of 645 letters in the dataset includes 18 Achus, 38 Hallus, 35 Othulu, 34×16 Guninthamulu, and 10 Ankelu. The proposed technique aims to efficiently recognize and identify distinctive Telugu characters online. This model's main pre-processing steps to achieve its goals include normalization, smoothing, and interpolation. Improved recognition performance can be attained by using stochastic gradient descent (SGD) to optimize the model's hyperparameters.


Convolutional neural networks; Data pre-processing; Deep neural networks; Network optimization; Optical character recognition; Telugu character recognition; Telugu letter dataset

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International Journal of Reconfigurable and Embedded Systems (IJRES)
p-ISSN 2089-4864, e-ISSN 2722-2608
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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