Automatic writer identification using connected-component contours and edge-based features of uppercase western script

Schomaker, L. & Bulacu, M., Jun-2004, In : Ieee transactions on pattern analysis and machine intelligence. 26, 6, p. 787-798 12 p.

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In this paper, a new technique for offline writer identification is presented, using connected-component contours (COCOCOs or CO(3)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 CO(3)s from an independent training set of 100 writers, the probability-density function (PDF) of CO(3)s was computed for an independent test set containing 150 unseen writers. Results revealed a high-sensitivity of the CO(3) 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 CO(3) PDF with an independent edge-based orientation and curvature PDF yielded very high correct identification rates.

Original languageEnglish
Pages (from-to)787-798
Number of pages12
JournalIeee transactions on pattern analysis and machine intelligence
Issue number6
Publication statusPublished - Jun-2004


  • writer identification, connected-component contours, edge-orientation features, stochastic allograph emission model, RECOGNITION, ONLINE, SKILL

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