CNN Model for Handwritten Digit Recognition with Improved Accuracy and Performance Using MNIST Dataset

Handwritten digit identification is an area that requires substantial attention. It is necessary to take into account the differences in handwritten digits’ size, thickness, position, and orientation when identifying them. With the advent of machine learning techniques, handwritten digit recognition is no longer as challenging as it once was. Handwritten numbers of various kinds that may be found in postal codes, addresses, historical archives, and medical records are all handled by the system. General performance across different handwriting styles is ensured by using machine learning algorithms, especially neural networks trained on big datasets. For improved performance and accuracy in the context of handwritten digit recognition, a Convolutional Neural Network (CNN) model is put forth and assessed in this study. With its sophisticated convolutional layers and optimal settings, our CNN architecture accurately captures some of the complex properties found in handwritten digits using the widely used MNIST dataset. The work not only advances computer vision research but also sheds light on how CNN architectures could be optimized for practical uses that call for accurate digit recognition. It controls the data and intricacies of diverse writing styles. Its versatility accounts for its exceptional digit recognition abilities in any given circumstance.