• Journal of Institute of Control, Robotics and Systems
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• v.7 no.11
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• pp.927-932
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• 2001

When people write a character with a pen stably, proper compliance planning is necessary. In this paper, after investigating the property of character writing task, we propose a fundamental grasp model for character writing and also analyze compliance characteristics for effective character writing using multi-fingered hands. For this, the general stiffness relation of multi-fingered hand is firstly described. Next, we investigate the grasp configurations for grasping a pen and then, we analyze the conditions of the specified stiffness matrix in the operational space to successfully and more effectively achieve the given character writing task. Through the analysis, an effective grasp modeling for successful character writing is shown. And also, we conclude that the operational compliance characteristics should be properly planned for character writing, stably and precisely.

• Journal of Korea Multimedia Society
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• v.14 no.5
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• pp.642-649
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• 2011

Among the various aspects of offline handwritten character patterns, it is the great variety of writing styles and variations that renders the task of computer recognition very hard. The immense variety of character shape has been recognized but rarely studied during the past decades of numerous research efforts. This paper tries to address the problem of measuring image distortions and handwritten character patterns with respect to reference patterns. This work is based on mass-spring mesh model with the introduction of simulated electric charge as a source of the external force that can aid decoding the shape distortion. Given an input image and a reference image, the charge is defined, and then the relaxation procedure goes to find the optimum configuration of shape or patterns of least potential. The relaxation process is based on the fourth order Runge-Kutta algorithm, well-known for numerical integration. The proposed method of modeling is rigorous mathematically and leads to interesting results. Additional feature of the method is the global affine transformation that helps analyzing distortion and finding a good match by removing a large scale linear disparity between two images.