Handwritten digit recognition using Convolutional Neural Network

Recognizing handwritten numbers is a daily necessity for systems in banking, education, and the post office, but because everyone's handwriting is unique, building an accurate automated system is very challenging.This research investigates the power of Convolutional Neural Networks (CNNs), finding them highly effective because they can automatically "see" and extract the distinct shapes and patterns of handwritten characters, making them the best method for this task.Our goal was to meticulously design and test a single, pure CNN architecture-exploring the best number of layers, filter sizes, and training methods-to achieve top performance without resorting to complex, slow, and costly "ensemble" (combined) architectures.After extensive experiments using the widely-used MNIST dataset and a new collection of diverse handwriting samples, we successfully demonstrated that our well-tuned, single CNN model not only offers practical efficiency but also achieves a remarkable recognition accuracy of 99.87% on the MNIST dataset, confirming that a simple, optimized CNN is the superior solution for this everyday automation problem.