Automatic Writer Identification Using Fragmented Connected-Component Contours

Authors:
Lambert Schomaker;Marius Bulacu;Katrin Franke
Affiliations:
AI Institute;AI Institute;AI Institute
Venue:
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
Year:
2004

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Abstract

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 be 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.