Handwritten Digit Recognition Using Machine Learning

The objective of this study is to implement a robust classification framework for handwritten digit recognition. The work investigates the performance of conventional machine learning algorithms, including Support Vector Machines (SVM), K-Nearest Neighbor (KNN), and Random Forest Classifier (RFC), alongside deep learning methods, specifically a multilayer Convolutional Neural Network (CNN) implemented using Keras with TensorFlow and Theano backends. Handwritten digit recognition remains a fundamental challenge in the field of pattern recognition and machine intelligence. In this paper, a novel framework is presented that combines deep learning techniques with an interactive Graphical User Interface (GUI). The CNN architecture is employed for automatic feature extraction and classification, thereby enhancing the accuracy and robustness of recognition. Furthermore, the integrated GUI enables real-time user interaction, allowing direct digit input and instant recognition feedback. Experimental evaluation has been conducted using the MNIST benchmark dataset. The obtained results demonstrate that the proposed system achieves competitive accuracy while simultaneously improving usability through real-time interaction. This integration of deep learning with an intuitive GUI highlights the potential of the proposed approach in practical handwritten digit recognition applications.