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IEEE Trans Pattern Anal Mach Intell. 2004 Jun;26(6):787-98. doi: 10.1109/TPAMI.2004.18.

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

Author information

1
AI Institute, Groningen University, Grote Kuisstraat 2/1, Groningen, The Netherlands. schomaker@ai.rug.nl

Abstract

In this paper, a new technique for offline writer identification is presented, using connected-component contours (COCOCOs or CO3s) 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 CO3s from an independent training set of 100 writers, the probability-density function (PDF) of CO3s was computed for an independent test set containing 150 unseen writers. Results revealed a high-sensitivity of the CO3 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 CO3 PDF with an independent edge-based orientation and curvature PDF yielded very high correct identification rates.

PMID:
18579938
DOI:
10.1109/TPAMI.2004.18
[Indexed for MEDLINE]

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