On graph-associated matrices and their eigenvalues for optical character recognition

In this paper, the classification power of the eigenvalues of six graph-associated matrices is investigated and evaluated on a benchmark dataset for optical character recognition. The extracted eigenvalues were utilized as feature vectors for multi-class classification using support vector machines. Each graph-associated matrix contains a certain type of geometric/spacial information, which may be important for the classification process. Classification results are presented for all six feature types, as well as for classifier combinations at decision level. For the decision level combination probabilistic output support vector machines have been applied. The eigenvalues of the weighted adjacency matrix provided the best classification rate of 89.9 %. Here, almost half of the misclassified letters are confusion pairs, such as I-L and N-Z. This classification performance can be increased by decision fusion, using the sum rule, to 92.4 %.