This project aims to implement classification algorithms specifically designed for recognizing handwritten digits, ranging from 0 to 9. Our approach includes an exploration of various combination methods of different classifiers, assessing their effectiveness in the context of handwritten digit recognition. We are utilizing Principal Component Analysis (PCA) and Support Vector Machines (SVM) to enhance recognition performance, achieving an accuracy rate of 94.9%.
