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Issue No.04 - April (2007 vol.29)
pp: 701-717
ABSTRACT
The identification of a person on the basis of scanned images of handwriting is a useful biometric modality with application in forensic and historic document analysis and constitutes an exemplary study area within the research field of behavioral biometrics. We developed new and very effective techniques for automatic writer identification and verification that use probability distribution functions (PDFs) extracted from the handwriting images to characterize writer individuality. A defining property of our methods is that they are designed to be independent of the textual content of the handwritten samples. Our methods operate at two levels of analysis: the texture level and the character-shape (allograph) level. At the texture level, we use contour-based joint directional PDFs that encode orientation and curvature information to give an intimate characterization of individual handwriting style. In our analysis at the allograph level, the writer is considered to be characterized by a stochastic pattern generator of ink-trace fragments, or graphemes. The PDF of these simple shapes in a given handwriting sample is characteristic for the writer and is computed using a common shape codebook obtained by grapheme clustering. Combining multiple features (directional, grapheme, and run-length PDFs) yields increased writer identification and verification performance. The proposed methods are applicable to free-style handwriting (both cursive and isolated) and have practical feasibility, under the assumption that a few text lines of handwritten material are available in order to obtain reliable probability estimates.
INDEX TERMS
Handwriting analysis, writer identification and verification, behavioral biometrics, joint directional probability distributions, grapheme-emission probability distribution.
CITATION
Marius Bulacu, Lambert Schomaker, "Text-Independent Writer Identification and Verification Using Textural and Allographic Features", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.29, no. 4, pp. 701-717, April 2007, doi:10.1109/TPAMI.2007.1009
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