Online Text-Independent Writer Identification Based on Stroke's Probability Distribution Function

This paper introduces a novel method for online writer identification. Traditional methods make use of the distribution of directions in handwritten traces. The novelty of this paper comes from 1)We propose a text-independent writer identification that uses handwriting stroke's probability distribution function (SPDF) as writer features; 2)We extract four dynamic features to characterize writer individuality; 3)We develop new distance measurement and combine dynamic features in reducing the number of characters required for online text-independent writer identification. In particular, we performed comparative studies of different similarity measures in our experiments. Experiments were conducted on the NLPR handwriting database involving 55 persons. The results show that the new method can improve the identification accuracy and reduce the number of characters required.

[1]  Hong Chang,et al.  SVC2004: First International Signature Verification Competition , 2004, ICBA.

[2]  Tieniu Tan,et al.  Biometric personal identification based on handwriting , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[3]  Graham Leedham,et al.  Writer identification using innovative binarised features of handwritten numerals , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[4]  R. Plamondon,et al.  The relation between pen force and pen-point kinematics in handwriting , 1990, Biological Cybernetics.

[5]  Horst Bunke,et al.  Writer identification using text line based features , 2001, Proceedings of Sixth International Conference on Document Analysis and Recognition.

[6]  Réjean Plamondon,et al.  Automatic signature verification and writer identification - the state of the art , 1989, Pattern Recognit..

[7]  Alberto Sanfeliu,et al.  Signatures versus histograms: Definitions, distances and algorithms , 2006, Pattern Recognit..

[8]  Tieniu Tan,et al.  Texture feature extraction via visual cortical channel modelling , 1992, Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol. III. Conference C: Image, Speech and Signal Analysis,.

[9]  Arun Ross,et al.  Score normalization in multimodal biometric systems , 2005, Pattern Recognit..

[10]  Alfred C. Weaver,et al.  Biometric authentication , 2006, Computer.

[11]  Jean-Pierre Crettez,et al.  A set of handwriting families: style recognition , 1995, Proceedings of 3rd International Conference on Document Analysis and Recognition.

[12]  Tieniu Tan,et al.  Personal identification based on handwriting , 2000, Pattern Recognit..

[13]  F. J. Maarse,et al.  Produced and perceived writing slant: difference between up and down strokes. , 1983, Acta psychologica.

[14]  Jin Hyung Kim,et al.  Statistical Character Structure Modeling and Its Application to Handwritten Chinese Character Recognition , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Claus Bahlmann,et al.  The writer independent online handwriting recognition system frog on hand and cluster generative statistical dynamic time warping , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Basil G. Mertzios,et al.  Statistical pattern recognition using efficient two-dimensional moments with applications to character recognition , 1993, Pattern Recognit..

[17]  Lambert Schomaker,et al.  Automatic writer identification using connected-component contours and edge-based features of uppercase Western script , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Tieniu Tan,et al.  Writer Identification Using Dynamic Features , 2004, ICBA.

[19]  Réjean Plamondon,et al.  Automatic Signature Verification: The State of the Art - 1989-1993 , 1994, Int. J. Pattern Recognit. Artif. Intell..

[20]  Lambert Schomaker,et al.  Automatic identification of writers , 1988 .