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Offline signature recognition is an important form of biometric identification that can be used for various purposes. Similar to other biometric measures, signatures have inherent variability and so pose a difficult recognition problem. In this paper, we explore a novel approach for reducing the variability associated with matching signatures based on curve warping. Existing techniques, such as the dynamic time warping approach, address this problem by minimizing a cost function through dynamic programming. This is by nature a 1-D optimization process that is possible when a 1-D parametrization of the curves is known. In this paper, we propose a novel approach for solving the curve correspondence problem that is not limited by the requirement of 1-D parametrization. The proposed approach utilizes particle dynamics and minimizes a cost function through an iterative solution of a system of first-order ordinary differential equations. The proposed approach is, therefore, capable of handling complex curves for which a simple parametrization is not available. The proposed approach is evaluated by measuring the precision and recall rates of documents based on signature similarity. To facilitate a realistic evaluation, the signature data we use were collected from real-world documents, spanning a period of several decades.