An efficient selected feature set for the middle age Persian character recognition

Abstract

In this paper, a morphological based method for recognition of handwritten middle Persian characters is presented. After pre-processing and noise cancellation, morphological erosion operator with many structure elements is applied. The structure elements are with variable length lines at directions 0, 45, 90, 135 degrees. A five element feature set has been defined so: (1) relative energy of eroded version with respect to the original image energy (REL/spl I.bar/ENG),(2) displacement of the center of mass (CM/spl I.bar//spl I.bar/DIS), (3) minimum eigenvalue (EIG/spl I.bar/MIN), (4) maximum eigenvalue (EIG/spl I.bar/MAX) and (5) its direction (EIG-DIR). These features are used to design a feedforward neural network with one hidden layer. The best classification error is about 2.39% (97.61% recognition rate), and is achieved with 150 neurons for the hidden layer.