Handwritten Digit Recognition Using SVM Binary Classifiers and Unbalanced Decision Trees

In this work, we use SVM binary classifiers coupled with a binary classifier architecture, an unbalanced decision tree, for handwritten digit recognition. According to input variables, two classifiers were trained and tested. One using digit characteristics and the other using the whole image as input variables. Developed recently, the unbalanced decision tree architecture provides a simple structure for a multiclass classifier using binary classifiers. In this work, using the whole image as input, 100% handwritten digit recognition accuracy was obtained in the MNIST database. These are the best results published in the literature for the MNIST database.