Deep Convolutional Neural Network with a Stochastic Gradient Descent Optimizer (PDCNN-SGD) model for telugu character recognition

Abstract

Telugu Character Recognition (TCR) has received significant attention because of the drastic increase in technological advancements such as multimedia, smartphones and iPods, and paper documents. Offline character recognition is the process of identifying Telugu characters from the scanned image or document whereas online character recognition enables to recognition of characters by the machine while the user writes. Several researchers have attempted to design online TCR models by the use of distinct classification models and feature extraction approaches. 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 preprocessing 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.