An Intelligent Handwritten Character Recognition Framework Using Dual Attention Vision Transformer-aided Adaptive Residual Dilated Dense Network in Telugu Scripts

Handwritten Telugu script's is a developing stage to provide substantial opportunities in further research and analysis. Optical Character Recognition (OCR) is considered as optical-based technology that includes a machine to automatically understand digitized and scanned characters. Yet, the limited research studies have been conducted in recent times for Hand Written Character Recognition (HWCR) models for the Telugu text. Hence, this research study implements an effective Telugu HWCR using adaptive deep learning network model. Initially, the raw images are taken from diverse benchmark datasets for further processing. Subsequently, the gathered images are fed into the developed recognition network, named Dual attention Vision Transformer-based Adaptive Residual Dilated Dense Network (DViT-ARDDNet). Here, the DViT technique initially splits the words into characters from the input handwritten images, allowing the RDDNet model effectively recognize the Telugu characters. Furthermore, the hyper-parameters are selected optimally by Statistically Improved Botox Optimization (SIBO). Therefore, the outcome of the model elucidates that the proposed network has the ability to appropriately identify the individual Telugu characters.