Ink-deposition model: the relation of writing and ink deposition processes

The paper describes our studies on the influence of physical and biomechanical processes on the ink trace and aims at providing a solid foundation for enhanced signature analysis procedures. By means of a writing robot, simulated human handwriting movements are considered to study the relation between writing process characteristics and ink deposit on paper. Since the robot is able to take up different writing instruments like pencil, ballpoint or fine line pen, the type of inking pen was also varied in the experiments. The results of analyzing these artificial ink traces contribute to a better understanding of the underlying interaction processes and allow for the formulation of a so-called ink-deposition model (IDM). Particularly, we present IDMs that analytically describe the relation of applied pen tip force and relative ink intensity distribution for solid, viscous and fluid ink types. These IDMs might be employed in computer-based analysis of ink trace line quality to recognize skilled forgeries.

[1]  Azriel Rosenfeld,et al.  Forgery Detection by Local Correspondence , 2001, Int. J. Pattern Recognit. Artif. Intell..

[2]  Angelo Chianese,et al.  Recovering dynamic information from static handwriting , 1993, Pattern Recognit..

[3]  I. S. I. Abuhaiba,et al.  Restoration of temporal information in off-line arabic handwriting , 1993, Pattern Recognit..

[4]  Robert Sabourin,et al.  Off-line Identification With Handwritten Signature Images: Survey and Perspectives , 1992 .

[5]  Pierre-Michel Lallican,et al.  Reconnaissance de l'ecriture manuscrite hors-ligne : utilisation de la chronologie restauree du trace , 1999 .

[6]  Stefan Jäger.  Recovering dynamic information from static, handwritten word images: bridging the gap between on-line and off-line handwriting recognition , 1998 .

[7]  Lambert Schomaker,et al.  Robotic writing trace synthesis and its application in the study of signature line quality , 2005 .

[8]  Katrin Franke,et al.  Towards an Universal Approach to Background Removal in Images of Bankchecks , 1999 .

[9]  Claudio M. Privitera,et al.  The segmentation of cursive handwriting: an approach based on off-line recovery of the motor-temporal information , 1999, IEEE Trans. Image Process..

[10]  Azriel Rosenfeld,et al.  Recovery of temporal information from static images of handwriting , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[11]  Sung-Hyuk Cha,et al.  Automatic detection of handwriting forgery , 2002, Proceedings Eighth International Workshop on Frontiers in Handwriting Recognition.

[12]  Sung-Hyuk Cha,et al.  Individuality of handwriting. , 2002, Journal of forensic sciences.

[13]  Sung-Hyuk Cha,et al.  Automatic Detection Of Handwriting Forgery Using A Fractal Number Estimate Of Wrinkliness , 2004, Int. J. Pattern Recognit. Artif. Intell..

[14]  Katrin Franke,et al.  Ink texture analysis for writer identification , 2002, Proceedings Eighth International Workshop on Frontiers in Handwriting Recognition.

[15]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

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