Handwritten Character Recognition Using Convolutional Neural Network for Asante Twi Language Alphabets

Abstract

Abstract The Handwritten Character Recognition (HWCR) system is a tool that recognizes handwritten characters in papers, photographs, and other sources and converts them into editable computer characters for future use. A significant barrier still exists in the accurate recognition of complex formed compound handwritten characters. By learning discriminatory qualities from vast volumes of raw data, convolutional neural network (CNN) technology has recently made significant advancements in HWCR systems. In identification of the Vernacular (Twi) language characters from off-line training and testing datasets, convolutional neural network was applied in this study. The primary goal of this research is to examine the convolutional neural network's ability to identify Twi characters from the supplied image datasets and the identification accuracy after training and testing. In order to differentiate among characters, convolutional neural network evaluates their differences in styles, shapes and topographies. To determine the accuracy of handwritten characters, the convolutional neural network implementation is tested using the novel Twi language dataset. On the basis of the test findings, a dataset set of 13, 200 images from the Twi dataset achieved an accuracy of 88.15% during training and a test accuracy of 79.31%. This study aids in knowing the suitable architecture that can be fused into an optical character recognition device to recognize Asante Twi handwritten letters found in documents of this particular language and translate them into editable formats.