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Table W contains the confusion matrix for the best experiment on the character segmentation problem.  \subsection{Analysis of experiments}  For the character recognition problem, using a RBF kernel SVM resulted in a 91\% \plusminus 0.126 error rate in the best case, using a value of 100 for the regularization rate, while using a linear kernel yielded 90.490\% \plusminus 0.097 as its best result, for the value of 1 for the regularization rate. Using a RBF kernel seems to be better. gives a slight improvement in performance.   In the case of the character segmentation, the ... had the most influence, ..., on the F1 score, while ... influenced it only by .... The highest average was obtained for the values of ... trees in the forest and ... features chosen at each split.