5.3. Training Data
Since the classifier is able to recognize damaged characters easily, the classifier was not trained on damaged characters. In fact, the classifier was trained on a mere 20 samples of 94 characters from 8 fonts in a single size, but with 4 attributes (normal, bold, italic, bold italic), making a total of 60160 training samples. This is a significant contrast to other published classifiers, such as the Calera classifier with more than a million samples [9], and Baird’s 100-font classifier [10] with 1175000 training samples.