• Publication
  • 2004
  • Issue No. 6 - June
  • Abstract - Automatic Writer Identification Using Connected-Component Contours and Edge-Based Features of Uppercase Western Script
 This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Automatic Writer Identification Using Connected-Component Contours and Edge-Based Features of Uppercase Western Script
June 2004 (vol. 26 no. 6)
pp. 787-798

Abstract—In this paper, a new technique for offline writer identification is presented, using connected-component contours (COCOCOs or CO^3s) 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 CO^3s from an independent training set of 100 writers, the probability-density function (PDF) of CO^3s was computed for an independent test set containing 150 unseen writers. Results revealed a high-sensitivity of the CO^3 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 CO^3 PDF with an independent edge-based orientation and curvature PDF yielded very high correct identification rates.

[1] S. Srihari, S. Cha, H. Arora, and S. Lee, Individuality of Handwriting J. Forensic Sciences, vol. 47, no. 4, pp. 1-17, July 2002.
[2] K. Franke and M. Köppen, A Computer-Based System to Support Forensic Studies on Handwritten Documents Int'l J. Document Analysis and Recognition, vol. 3, no. 4, pp. 218-231, 2001.
[3] H. Said, T. Tan, and K. Baker, Writer Identification Based on Handwriting Pattern Recognition, vol. 33, no. 1, pp. 133-148, 2000.
[4] U.-V. Marti, R. Messerli, and H. Bunke, Writer Identification Using Text Line Based Features Proc. Sixth Int'l Conf. Document Analysis and Recognition (ICDAR '01), pp. 101-105, 2001.
[5] Y. Zhu, T. Tan, and Y. Wang, Biometric Personal Identification Based on Handwriting Proc. 15th Int'l Conf. Pattern Recognition, pp. 801-804, 2000.
[6] M. Benecke, DNA Typing in Forensic Medicine and in Criminal Investigations: A Current Survey Naturwissenschaften, vol. 84, no. 5, pp. 181-188, 1997.
[7] B. Devlin, N. Risch, and K. Roeder, Forensic Inference from DNA Fingerprints J. Am. Statistical Assoc., vol. 87, no. 418, pp. 337-350, 1992.
[8] A.K. Jain, L. Hong, and R. Bolle, On-Line Fingerprint Verification IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 4, pp. 302-314, Apr. 1997.
[9] M. E, V. Ballarin, F. Pessana, S. Torres, and D. Olmo, Fingerprint Identification Using Image Enhancement Techniques J. Forensic Sciences, vol. 43, no. 3, pp. 689-692, 1998.
[10] J. Daugman, The Importance of Being Random: Statistical Principles of Iris Recognition Pattern Recognition, vol. 36, no. 2, pp. 279-291, 2003.
[11] L.R.B. Schomaker, “From Handwriting Analysis to Pen-Computer Applications,” Electronic and Communication Eng. J., pp. 93-102, June 1998.
[12] E. Dooijes, Analysis of Handwriting Movements Acta Psychologica, vol. 54, pp. 99-114, 1983.
[13] C. Francks, L. DeLisi, S. Fisher, S. Laval, J. Rue, J. Stein, and A. Monaco, Confirmatory Evidence for Linkage of Relative Hand Skill to 2p12-q11 Am. J. Human Genetics, vol. 72, pp. 499-502, 2003.
[14] J. Gulcher, P. Jonsson, A. Kong et al., Mapping of a Familial Essential Tremor Gene, Fet1, to Chromosome 3q13 Nature Genetics, vol. 17, no. 1, pp. 84-87, 1997.
[15] G.P. Van Galen, J. Portier, B.C.M. Smits-Engelsman, and L. Schomaker, Neuromotor Noise and Poor Handwriting in Children Acta Psychologica, vol. 82, pp. 161-178, 1993.
[16] E. Moritz, Replicator-Based Knowledge Representation and Spread Dynamics Proc. IEEE Int'l Conf. Systems, Man, and Cybernetics, pp. 256-259, 1990.
[17] G. Jean, Writing: The Story of Alphabets and Scripts. Thames and Hudson Ltd., 1997.
[18] L.R.B. Schomaker and R. Plamondon, The Relation between Pen Force and Pen-Point Kinematics in Handwriting Biological Cybernetics, vol. 63, pp. 277-289, 1990.
[19] L. Schomaker, Simulation and Recognition of Handwriting Movements: A Vertical Approach to Modeling Human Motor Behavior PhD dissertation, Univ. of Nijmegen, NICI, The Netherlands, 1991.
[20] R. Schmidt, A Schema Theory of Discrete Motor Skill Learning Psychological Rev., vol. 82, pp. 225-260, 1975.
[21] L. Schomaker, A. Thomassen, and H.-L. Teulings, A Computational Model of Cursive Handwriting Computer Recognition and Human Production of Handwriting, M.S.R. Plamondon and C.Y. Suen, eds., World Scientific, pp. 153-177, 1989.
[22] R. Plamondon and F.J. Maarse, “An Evaluation of Motor Models of Handwriting,” IEEE Trans. Systems, Man, and Cybernetics, vol. 19, no. 5, pp. 1,060-1,072, 1989.
[23] R. Plamondon and W. Guerfali, The Generation of Handwriting with Delta-Lognormal Synergies Biological Cybernetics, vol. 78, pp. 119-132, 1998.
[24] D.S. Doermann and A. Rosenfeld, “Recovery of Temporal Information from Static Images of Handwriting,” Proc. Computer Soc. Conf. Computer Vision and Pattern Recognition, pp. 162-168, 1992.
[25] K. Franke and G. Grube, The Automatic Extraction of Pseudo-Dynamic Information from Static Images of Handwriting Based on Marked Gray Value Segmentation J. Forensic Document Examination, vol. 11, pp. 17-38, 1998.
[26] S. Kondo and B. Attachoo, Model of Handwriting Process and Its Analysis Proc. Eighth Int'l Conf. Pattern Recognition, pp. 562-565, 1986.
[27] L.G. Vuurpijl and L.R.B. Schomaker, Finding Structure in Diversity: A Hierarchical Clustering Method for the Categorization of Allographs in Handwriting Proc. Fourth Int'l Conf. Document Analysis and Recognition, pp. 387-393, 1997.
[28] L. Schomaker, M. Bulacu, and M. van Erp, Sparse-Parametric Writer Identification Using Heterogeneous Feature Groups Proc. IEEE Int'l Conf. Image Processing, vol. 1, pp. 545-548, 2003.
[29] T. Kohonen, Self-Organization and Associative Memory, second ed., Berlin: Springer Verlag, 1988.
[30] L.R.B. Schomaker, Using Stroke- or Character-Based Self-Organizing Maps in the Recognition of On-Line, Connected Cursive Script Pattern Recognition, vol. 26, no. 3, pp. 443-450, 1993.
[31] L. Schomaker, G. Abbink, and S. Selen, Writer and Writing-Style Classification in the Recognition of Online Handwriting Proc. European Workshop Handwriting Analysis and Recognition: A European Perspective. Digest Number 1994/123, p. 4, July 1994.
[32] L. Schomaker, E. de Leau, and L. Vuurpijl, Using Pen-Based Outlines for Object-Based Annotation and Image-Based Queries Visual Information and Information Systems, D. Huijsmans and A. Smeulders, eds., New York: Springer, pp. 585-592, 1999.
[33] F. Maarse, L. Schomaker, and H.-L. Teulings, Automatic Identification of Writers Human-Computer Interaction: Psychonomic Aspects, G. van der Veer and G. Mulder, eds., New York: Springer, pp. 353-360, 1988.
[34] J.-P. Crettez, A Set of Handwriting Families: Style Recognition Proc. Third Int'l Conf. Document Analysis and Recognition, pp. 489-494, Aug. 1995.
[35] F. Maarse and A. Thomassen, Produced and Perceived Writing Slant: Differences between Up and Down Strokes Acta Psychologica, vol. 54, nos. 1-3, pp. 131-147, 1983.
[36] M. Bulacu, L. Schomaker, and L. Vuurpijl, Writer Identification Using Edge-Based Directional Features Proc. ICDAR'2003: Int'l Conf. Document Analysis and Recognition, pp. 937-941, 2003.
[37] M. Bulacu and L. Schomaker, Writer Style from Oriented Edge Fragments Proc. 10th Int'l Conf. Computer Analysis of Images and Patterns, pp. 460-469, 2003.
[38] L. Vuurpijl, L. Schomaker, and V. Erp, Architecture for Detecting and Solving Conflicts: Two-Stage Classification and Support Vector Classifiers Int'l J. Document Analysis and Recognition, vol. 5, no. 4, pp. 213-233, 2003.
[39] A. Broeders, In Search of the Source: On the Foundations of Criminalistics and the Assessment of Forensic Evidence PhD thesis, Leiden Univ., with abstract in English, ISBN 90-130-0964-6, Netherlands: Kluwer, p. 349, 2003.
[40] G. Davis and A. Nosratinia, Wavelet-Based Image Coding: An Overview Applied and Computational Control, Signals, and Circuits, vol. 1, no. 1, 1998.
[41] M. van Erp, L. Vuurpijl, K. Franke, and L. Schomaker, The WANDA Measurement Tool for Forensic Document Examination Proc. 11th Conf. Int'l Graphonomics Soc., pp. 282-285, 2003.

Index Terms:
Writer identification, connected-component contours, edge-orientation features, stochastic allograph emission model.
Citation:
Lambert Schomaker, Marius Bulacu, "Automatic Writer Identification Using Connected-Component Contours and Edge-Based Features of Uppercase Western Script," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 6, pp. 787-798, June 2004, doi:10.1109/TPAMI.2004.18
Usage of this product signifies your acceptance of the Terms of Use.