(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 13, No. 4, 2022 703 | Page www.ijacsa.thesai.org A CNN based Approach for Handwritten Character Identification of Telugu Guninthalu using Various Optimizers B. Soujanya 1 Assistant Professor, Dept of Computer Science and Engineering Institute of Technology, GITAM (Deemed to be University, Visakhapatnam, India Suresh Chittineni 2 , T. Sitamahalakshmi 3 Professor, Dept of Computer Science and Engineering Institute of Technology, GITAM (Deemed to be University Visakhapatnam, India G. Srinivas 4 Associate Professor, Dept of Computer Science and Engineering, Institute of Technology, GITAM (Deemed to be University, Visakhapatnam, India 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 I. INTRODUCTION In today's world, the internet is brimming with images and video representations, providing sufficient opportunity for building numerous research applications for image and video analysis [1] to educate people about more complex material and techniques. With the rise of Artificial Neural Networks, machine learning has advanced significantly in recent years (ANN). These ideas enhance the model's capabilities beyond machine-learning tasks and other domains. Convolutional neural network (CNN) architecture has been considered as one of the most inventive. Using CNN in image processing became clearer and more beneficial as ANN performance deteriorated in object recognition and image classification. As better CNN became accessible, research using CNN in image processing domains grew dramatically [2-4]. CNNs have had a lot of success in various domains, including computer vision, natural language processing, and speech recognition. One of the most widely used machine learning models is CNN which has been expanded to handle a wide range of visual image applications, item classification, and audio identification challenges by applying mathematical representations. Multi-layer network structure that may be learned and consists of several layers [5]. Raw pixel values may be utilized as input to the network instead of feature vectors, which are often employed in machine learning. Even though there are many different kinds of CNNs (fig.1), they always have the same basic structure: a convolutional layer, a pooling layer, and an entirely connected layer. 1) Convolution layer: Images are filtered using this tool, which identifies characteristics that are used to identify matching spots during testing. Enlarged images need a convolution procedure with minimum parameters. With a filter or kernel, the input data is transformed into a feature map for use by CNN. 2) Pooling layer: This layer receives the extracted characteristics. It reduces bigger images while keeping the most critical data. It keeps the maximum value from each window by preserving the best fit value. This function shrinks the picture spatially to minimize the number of parameters and computations in the model. Max Pooling is a typical strategy in pooling. It selects the greatest element from the feature map covered by the filter. 3) Fully connected layer: High-level filtered images are fed in and categorized using labels in the final layer. Every neuron in this layer is related to the one below it. Layers of convolution and pooling are common in most designs. Fig. 1. Layers CNN.