New Visual Features for Farsi Handwriting Images based Graphology Study of Personality using Image Processing for Farsi Handwriting

Document Type : Research Paper



In this paper, we propose a system for recognizing static signs of Persian sign language (PSL). This system, designed based on a novel geometric feature, can automatically recognize static signs of Persian sign language alphabet with high accuracy. In feature extraction stage, we first find the center of gravity and contour of the hand shape in images, and then sample from obtained hand contour points. Then we consider a circle for each point on the contour. For this purpose, we compute the distance between the center of gravity of hand shape and each point along the contour. We select half of the computed distance for each point and the point located in the middle of this distance as radius and center of circle respectively. The resulting circles contain valuable information about the shape of hand that they are organized as feature vector for each sign. To recognize signs with high reliability, we propose a system that combine support vector machine (SVM) with K nearest neighbor (KNN). Experiments are performed on PSL database. The accuracy and reliability of proposed system with the test data are 93.33% and 98.73% respectively. The obtained results show the performance of proposed system is satisfactory.