Analyses and Tests of Handwritten Digit Recognition Algorithms

This report is a masters thesis written at the Department of Mathematics, Linköping University. Two different classification algorithms for handwritten digit recognition have been thoroughly analysed. The first algorithm uses Higher Order Singular Value Decomposition (HOSVD) of the training digits. The second algorithm relies on a specific distance measure, which is invariant to different transformations, called Tangent Distance (TD). This algorithm was modified in the implementation part by the use of numerical derivatives and an approximation of the blurring operator. Two more classification algorithms were constructed by combining the first two algorithms. All constructed algorithms have been tested with good performance for some of them. The best results were achieved by the Tangent Distance classifier with an error rate of 3 %. Finally the results of a few other classifiers are presented and compared with the test results obtained in this report