Automatic Writer Identification Using Fragmented Connected-Component Contours

In this paper, a method for off-line writer identification is presented, using the contours of fragmented connected components in mixed-style handwritten samples of limited size. The writer is considered to characterized by a stochastic pattern generator, producing a family of character fragments (fraglets). Using a codebook of such fraglets from an independent training set, the probability distribution of fraglet contours was computed for an independent test set. Results revealed a high sensitivity of the fraglet histogram in identifying individual writers on the basis of a paragraph of text. Large-scale experiments on the optimal size of Kohonen maps of fraglet contours were performed, showing usable classification rates within a non-critical range of Kohonen map dimensions. Further validation experiments on variable-sized random subsets from an independent set of 215 writers gives additional support for the proposed method. The proposed automatic approach bridges the gap between image statistics approaches and manual character-based methods.