Comparative evaluation of different classifiers for robust distorted-character recognition

This paper investigates and compares between applying the algorithms of Support Vector Machine (SVM), Principal Component Analysis (PCA), Individual Principal Component Analysis (iPCA), Linear Discriminant Analysis (LDA), and Single-Nearest-Neighbor Method (1-NNM) to distorted-character recognition. Applying SVM achieves a classification error rate of 2.15% on the Letter-Image Dataset [Frey and Slate 1991]. This error rate is statistically comparable to the best number in the literature on this dataset that the authors are aware of, which is 2%. This was achieved by a fully connected MLP neural network with adaboosting, where training was performed on 20 machines [Schwenk and Bengio 1997]. However, using SVM on a single machine, takes less than 3.5 minutes for training. The features of the dataset and the errors committed by SVM were analyzed in an attempt to combine classifiers and reduce the error rate. We report the results achieved for the different techniques used.