Enhanced Handwritten Digit Recognition Using Artificial Neural Networks

Abstract

Handwritten digit recognition plays a vital role in numerous applications such as postal automation, bank check processing, and digitized document management. In this paper, we introduce an enhanced approach to handwritten digit recognition by leveraging Artificial Neural Networks (ANNs). Our proposed methodology integrates state-of-the-art ANN architectures and preprocessing techniques aimed at improving recognition accuracy and efficiency. Through rigorous experimentation conducted on benchmark dataset MNIST our method achieves an impressive accuracy of 94.51%. This accuracy surpasses that of traditional approaches, underscoring the effectiveness of employing ANNs with optimized configurations and preprocessing methods for handwritten digit recognition tasks. These findings demonstrate the potential of our approach for real-world applications requiring robust and efficient digit recognition systems, further advancing the field of character recognition technology.