Abstract—Dealing with hundreds of features in character recognition systems is not unusual. This large number of features leads to the increase of computational workload of recognition process. There have been many methods which try to remove unnecessary or redundant features and reduce feature dimensionality. Besides because of the characteristics of Farsi scripts, it’s not possible to apply other languages algorithms to Farsi directly. In this paper some methods for feature subset selection using genetic algorithms are applied on a Farsi optical character recognition (OCR) system. Experimental results show that application of genetic algorithms (GA) to feature subset selection in a Farsi OCR results in lower computational complexity and enhanced recognition rate.

In this paper, a new robot massage experimental setup for leg using genetic algorithm based camera calibration is presented. Teach Mover, a five axis articulated robot is used to press the muscle from ankle to knee. The real leg massage problem is approximated by a frustum shaped model, which can be easily extended to real leg massage. Three different sensors that are encoders; mounted at each joint of the robot with six degrees of freedom, a calibrated camera and a grip switch; mounted at the wrist of the manipulator were used. Camera calibration is done with the help of an algorithm proposed by Qiang Ji et. al [1] to estimate internal and external camera parameters using seven control points. The distance between camera and the robot is assumed to be fixed. By estimating the position and orientation of the object, which is the frustum model, the linear trajectory is found which the robot follows. The result shows the feasibility of the use of above-mentioned approach. The algorithm works satisfactorily for wide range of varying parameters i.e. the position and orientation of the model.