Composite Method for Printed Written Number Identification

Real-time handwritten digit recognition is vital for applications including automated form processing, postal code interpretation, and banking systems. This paper introduces a deep learning method utilizing a Convolutional Neural Network (CNN) trained on a mix of the MNIST dataset and a real-world handwritten digit dataset featuring over 20,000 samples sourced from Kaggle. The model is created with comprehensive preprocessing and data augmentation strategies such as rotation, zoom, and shifting to improve its robustness and generalization across diverse writing styles. The implementation encompasses real-time image capturing, thorough training, and evaluation employing tools like TensorFlow and Keras. A comparative analysis with traditional models such as SVM and KNN emphasizes CNN’s superior performance concerning accuracy, loss, and training duration. A feedback-driven improvement method further enhances the model by learning from user-reported misclassifications, thus boosting adaptability over time. Through this research, we provide a scalable and intelligent digit recognition system designed for real-time application.