Combining Multiple Features for Text-Independent Writer Identification and Verification

Conference object English OPEN
Bulacu , Marius ; Schomaker , Lambert (2006)
  • Publisher: Suvisoft
  • Subject: ACM : I.: Computing Methodologies/I.5: PATTERN RECOGNITION | Writer identification and verification | directional | [ INFO.INFO-TT ] Computer Science [cs]/Document and Text Processing | [ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] | feature combination | ACM : I.: Computing Methodologies/I.7: DOCUMENT AND TEXT PROCESSING | grapheme | run-length probability distributions
    acm: ComputingMethodologies_DOCUMENTANDTEXTPROCESSING; In recent years, we proposed a number of new and very effective features for automatic writer identification and verification. They are probability distribution functions (PDFs) extracted from the handwriting images and characterize writer individuality independently of the textual content of the written samples. In this paper, we perform an extensive analysis of feature combinations. In our fusion scheme, the final unique distance between two handwritten samples is computed as the average of the distances due to the individual features participating in the combination. Obtained on a large dataset containing 900 writers, our results show that fusing multiple features (directional, grapheme, run-length PDFs) yields increased writer identification and verification performance.
  • References (16)
    16 references, page 1 of 2

    [1] B. Arazi. Handwriting identi cation by means of runlength measurements. IEEE Trans. Syst., Man and Cybernetics, SMC-7(12):878–881, 1977.

    [2] A. Bense a, T. Paquet, and L. Heutte. A writer identi cation and veri cation system. Pattern Recognition Letters, 26(10):2080–2092, 2005.

    [3] M. Bulacu and L. Schomaker. A comparison of clustering methods for writer identi cation and veri cation. In Proc. of 8th ICDAR, pages 1275–1279, 2005.

    [4] M. Bulacu, L. Schomaker, and L. Vuurpijl. Writer identication using edge-based directional features. In Proc. of 7th ICDAR, pages 937–941, 2003.

    [5] I. Dinstein and Y. Shapira. Ancient hebraic handwriting identi cation with run-length histograms. IEEE Trans. Syst., Man and Cybernetics, SMC-12(3):405–409, 1982.

    [6] F. Maarse, L. Schomaker, and H.-L. Teulings. Automatic identi cation of writers. In G. van der Veer and G. Mulder, editors, Human-Computer Interaction: Psychonomic Aspects, pages 353–360. Springer, New York, 1988.

    [7] U.-V. Marti and H. Bunke. The IAM-database: an english sentence database for off-line handwriting recognition. Int. J. on Doc. Analysis and Recognition, 5(1):39–46, 2002.

    [8] R. Plamondon and G. Lorette. Automatic signature veri - cation and writer identi cation - the state of the art. Pattern Recognition, 22(2):107–131, 1989.

    [9] H. Said, T. Tan, and K. Baker. Personal identi cation based on handwriting. Pattern Recognition, 33:149–160, 2000.

    [10] A. Schlapbach and H. Bunke. Using HMM-based recognizers for writer identi cation and veri cation. In Proc. of 9th IWFHR, pages 167–172, 2004.

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