TY - JFULL
AU - Abdullah A. AlShaher
PY - 2018/1/
TI - Arabic Character Recognition Using Regression Curves with the Expectation Maximization Algorithm
T2 - International Journal of Computer and Information Engineering
SP - 1086
EP - 1091
VL - 12
SN - 1307-6892
UR - https://publications.waset.org/pdf/10009888
PU - World Academy of Science, Engineering and Technology
NX - Open Science Index 144, 2018
N2 - 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 2nd 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 then proceed 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.
ER -