CNN-Based Approach for Handwritten Digit Recognition

Handwritten digit recognition is a widely studied problem in the field of computer vision due to its practical relevance in real-world applications such as document digitization, automated form processing, banking systems, and postal services. Despite the apparent simplicity of recognizing numerical characters, variations in individual handwriting styles, stroke patterns, and image quality make automated recognition a challenging task. In this work, a handwritten digit recognition system based on a Convolutional Neural Network (CNN) is designed and implemented. The proposed model is trained and evaluated using the MNIST dataset, consisting of grayscale images of digits from 0 to 9. Experimental evaluation shows that the developed CNN model achieves high classification accuracy with stable performance. The results indicate that the system is effective for handwritten digit recognition tasks and is well suited for real-world applications as well as final-year undergraduate academic projects.