Abstract: 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.
Abstract: In this paper, a new proposed system for Persian
printed numeral characters recognition with emphasis on
representation and recognition stages is introduced. For the first time,
in Persian optical character recognition, geometrical central moments
as character image descriptor and fuzzy min-max neural network for
Persian numeral character recognition has been used. Set of different
experiments on binary images of regular, translated, rotated and
scaled Persian numeral characters has been done and variety of
results has been presented. The best result was 99.16% correct
recognition demonstrating geometrical central moments and fuzzy
min-max neural network are adequate for Persian printed numeral
character recognition.