Automatic writer identification using connected-component contours and edge-based features of uppercase Western script
Schomaker, L.
Bulacu, M.
AI Inst., Groningen Univ., Netherlands;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: June 2004
Volume: 26,
Issue: 6
On page(s): 787-798
ISSN: 0162-8828
INSPEC Accession Number: 8027544
Digital Object Identifier: 10.1109/TPAMI.2004.18
Current Version Published: 2004-04-19
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 CC's 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.
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