Shape Recognition using Regression Curves with the EM Algorithm

Abdullah A Al-Shaher¹

¹Department of Computers and Information Systems, College of Business Studies,
Public Authority for Applied Education and Training, Kuwait

Abstract

In this paper, we demonstrate how regression curves can be used to recognize 2D non-rigid handwritten shapes. Each shape is represented by a set of non-overlapping uniformly distributed landmarks. The underlying models utilize 2^nd order of polynomials to model shapes within a training set. To estimate the regression models, we need to extract the required coefficients which describe the variations for a set of shape class. Hence, a least square method is used to estimate such modes. We proceed then, by training these coefficients using the apparatus Expectation Maximization algorithm. Recognition is carried out by finding the least error landmarks displacement with respect to the model curves. Handwritten isolated Arabic characters are used to evaluate our approach.

Keywords

• Character Recognition Pattern Recognition Shape Analysis Image Processing Arabic Handwritten Characters

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Cite this Article as Follows

Abdullah A Al-Shaher. Shape Recognition using Regression Curves with the EM Algorithm. International Conference on Computer and Computing Science (2019): 01. Print.