Handwritten English Digit Recognition

Understanding handwritten digits through auto- mated systems is a fundamental requirement in many intelligent applications. This work introduces a deep learning–based solution for recognizing handwritten English digits using the Convolutional Network (CNN) architecture. The model which we are using is trained and validated using the MNIST dataset, which consists of grayscale digit images covering numbers from zero through nine. In contrast to traditional approaches that rely on manually crafted features, the system which we have proposed learns meaningful representations directly from image pixels. Experimental results describe consistent accuracy of results across diverse handwriting styles. The developed model can be effectively applied in automated document processing, form digitization, and financial data entry, while also providing a scalable base for future handwritten text recognition systems.