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<p><b>Abstract</b>—In this paper, a new technique for offline writer identification is presented, using connected-component contours (COCOCOs or <tmath>CO^3</tmath>s) in uppercase handwritten samples. In our model, the writer is considered to be characterized by a stochastic pattern generator, producing a family of connected components for the uppercase character set. Using a codebook of <tmath>CO^3</tmath>s from an independent training set of 100 writers, the probability-density function (PDF) of <tmath>CO^3</tmath>s was computed for an independent test set containing 150 unseen writers. Results revealed a high-sensitivity of the <tmath>CO^3</tmath> PDF for identifying individual writers on the basis of a single sentence of uppercase characters. The proposed automatic approach bridges the gap between image-statistics approaches on one end and manually measured allograph features of individual characters on the other end. Combining the <tmath>CO^3</tmath> PDF with an independent edge-based orientation and curvature PDF yielded very high correct identification rates.</p>
Writer identification, connected-component contours, edge-orientation features, stochastic allograph emission model.

L. Schomaker and M. Bulacu, "Automatic Writer Identification Using Connected-Component Contours and Edge-Based Features of Uppercase Western Script," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 26, no. , pp. 787-798, 2004.
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