Real-Time Handwritten Digit Recognition using CNNs and Web Interface

Handwritten digit recognition plays a vital role in several automation-based applications, including postal code sorting, digital form processing, and banking systems. This study presents a real-time digit recognition framework that uses a Convolutional Neural Network (CNN) combined with an interactive web interface to classify digits drawn or uploaded by users. The CNN is trained on the MNIST dataset and enhanced using preprocessing and normalization techniques to improve recognition accuracy across diverse handwriting styles. A lightweight Flask backend processes user inputs, performs inference, and returns instant predictions to the web interface. The system demonstrates efficient real-time classification and high accuracy, highlighting its potential for deployment in educational tools, automated document digitization, and user-interactive applications.