Allographic fraglet codebooks for writer identification

Personal and individual writing style is not only characterized by slant and roundness but also by typical character shapes for writing a letter: Allographs. If a complete list of allographs were available, this would help in writer identification. However, before such a list is compiled from the writer population, one wants to be able to count the occurrence of typical shapes in a writing style. By applying an oversegmentation heuristic to scanned handwriting, fragmented connected components can be extracted from a sample. The shape of these fraglets can be characterized by their contour (Schomaker & Bulacu, 2004). First a codebook is computed of a few hundred prototypical fraglets, on the basis of clustering a very large reference data set. Then, for a given writer, the histogram of fraglet usage can be computed. The resulting vector is a very useful feature in writer identification and verification. If combined with other features, such as the hinge transform and/or horizontal run-length histograms, the overall system will provide a comprehensive view on writing aspects, with a consequently high writer-identification rate.

Relevant papers:

• Schomaker, L.R.B. & Bulacu, M. (2004). Automatic writer identification using connected-component contours and edge-based features of upper-case Western script. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 26(6), June 2004, pp. 787 - 798.
• Bulacu, M. & Schomaker, L.R.B. (2007). Text-independent Writer Identification and Verification Using Textural and Allographic Features, IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), Special Issue - Biometrics: Progress and Directions, April, 29(4), p. 701-717.
• Bulacu, M. & Schomaker, L.R.B. (2003). Writer Style from Oriented Edge Fragments. In: N. Petkov & M.A. Westenberg (Eds.), LNCS 2756 - Computer Analysis of Images and Patterns, pp. 460-469.
• Schomaker, L.R.B. (1993). Using Stroke- or Character-based Self-organizing Maps in the Recognition of On-line, Connected Cursive Script. Pattern Recognition , 26(3), 443-450.