Advanced Neural Networks for Handwritten Digit Classification

Abstract

Recognition of handwritten digits remains one of the most important tasks of deep learning and computer vision and is commonly used in postal services, banking, and other automated systems. For this project, CNNs were used in order to achieve precise results in terms of handwritten digit recognition. This model was trained using the MNIST dataset which contains 60,000 training images and 10,000 testing images. The CNN model is capable of extracting features from handwritten digits with high levels of accuracy making the classification highly precise. The proposed system will improve recognition accuracy, minimize the model's computational complexity, and enhance the efficiency of the system. Using modern neural networks in this manner guarantees increased generalization and real-world usability of the model. The outcome showed a clear advancement in the accuracy of automated digit classification, which can certainly be used to enhance automated recognition systems.