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

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%.