TY - JFULL
AU - A. Harifi and A. Aghagolzadeh
PY - 2007/4/
TI - A New Pattern for Handwritten Persian/Arabic Digit Recognition
T2 - International Journal of Computer and Information Engineering
SP - 797
EP - 801
VL - 1
SN - 1307-6892
UR - https://publications.waset.org/pdf/14864
PU - World Academy of Science, Engineering and Technology
NX - Open Science Index 3, 2007
N2 - The main problem for recognition of handwritten Persian digits using Neural Network is to extract an appropriate feature vector from image matrix. In this research an asymmetrical segmentation pattern is proposed to obtain the feature vector. This pattern can be adjusted as an optimum model thanks to its one degree of freedom as a control point. Since any chosen algorithm depends on digit identity, a Neural Network is used to prevail over this dependence. Inputs of this Network are the moment of inertia and the center of gravity which do not depend on digit identity. Recognizing the digit is carried out using another Neural Network. Simulation results indicate the high recognition rate of 97.6% for new introduced pattern in comparison to the previous models for recognition of digits.
ER -