Handwritten Digit Segmentation in Images of Historical Documents with One-Class Classifiers

A novel method is proposed herein for handwritten digit segmentation in historical document images. It is based on one-class classifiers, which are used to distinguish isolated characters from touching characters. In contrast to other techniques based on feed forward neural networks, the proposed method does not require negative data in the training phase. Three methods for feature extraction and five one class classifiers are considered and have their performance compared. Experimental results on a data set of handwritten digits extracted from a collection of historical documents show the effectiveness of the proposed method.