Handwritten Character Recognition Using Elastic Matching Based On Class-Dependent Deformation Model

For handwritten character recognition, a new elastic image matching (EM) technique based on a class-dependent deformation model is proposed. In the deformation model, any deformation of a class is described by a linear combination of eigen-deformations, which are intrinsic deformation directions of the class. The eigen-deformations can be estimated statistically from the actual deformations of handwritten characters. Experimental results show that the proposed technique can attain higher recognition rates than conventional EM techniques based on class-independent deformation models. The results also show the superiority of the proposed technique over those conventional EM techniques in computational efficiency

Piecewise Linear Two-Dimensional Warping

A new efficient dynamic programming (DP) algorithm for 2D elastic matching is proposed. The present DP algorithm requires by far less complexity than previous DPbased elastic matching algorithms. This complexity reduction results from piecewise linearization of a 2D-2D mapping which specifies an elastic matching between two given images. Since this linearization can be guided by a priori knowledge related to image patterns to be matched, the present DP algorithm often provides sufficient matching as is shown by experimental results. 1. Introduction Two-dimensional (2D) elastic matching, or deformable template is one of the most fundamental techniques for pattern recognition and image analysis [1, 2, 3, 4]. In 2D elastic matching, one image is linearly or nonlinearly warped and then fitted to another image by a 2D-2D mapping called 2D warping (2DW). Generally, 2DW is determined by solving a pixel-to-pixel correspondence optimization problem and therefore the characteristics of the 2DW..

Handwritten Character Recognition Using Piecewise Linear Two-Dimensional Warping

In this paper, the e#ectiveness of piecewise linear two-dimensional warping, a dynamic programming-based elastic image matching technique, in handwritten character recognition is investigated. The present technique is capable of providing compensation for most variations in character patterns while its computation remains tractable. The superiority of the present technique over several conventional two-dimensional warping techniques in providing deformation compensation is justified by experimental results with English alphabet. Another comparison with monotonic and continuous two-dimensional warping, a more flexible matching technique, reveals that the present method takes far less computation than the latter, yet provides almost the same recognition accuracy for most categories