Coarse Writing-style Clustering Based on Simple Stroke-related Features

Two methods are presented for the automatic detection of generic writing styles like e.g. mixed, cursive and handprint. Based on a set of handwritten words, three features are determined: a cursivity index , which indicates the tendency of a writer to write cursive, and two distance measures and . The distance measures represents the distance between the stroke feature vectors in the input handwriting and the strokes contained in two style-specific Kohonen Self-Organizing Maps (SOM). One SOM is tuned for the writing style handprint, and the other for cursive. The first method uses some linear decision criteria based on the feature vector , for the classification of one of the three writing styles. The second method uses nonlinear decision boundaries found via agglomerative hierarchical clustering of the three-dimensional feature vectors. Using the second method, several more distinctive writing style classifications are proposed.