Hand Written Character Recognition Using CNN Model

Abstract

Abstract: Handwritten character recognition (HWR) plays a critical role in document processing, robotic automation, and historical document analysis. While traditional methods relied on template matching and feature engineering, these often struggled with diverse writing styles and noise. Convolutional neural networks (CNNs) have emerged as a powerful alternative, achieving remarkable accuracy in HWR tasks. This paper delves into enhancing HWR performance using CNNs on the EMNIST Balanced dataset. We investigate the impact of data augmentation techniques on model generalizability and propose a custom architecture optimized for the task. Through detailed analysis of performance metrics, confusion matrices, and visualization of predictions, we gain valuable insights into the model's behavior and potential areas for improvement.