Arabic manuscripts identification based on Feature Relation Graph

Abstract

We investigate a new metric based on the Feature Relation Graph (FRG). This metric has proved to be effective for the text independent Persian writer identification. Since Persian script is based on Arabic writing similar principles of analysis may be also applied to the Arabic manuscripts. We have investigated the FRG for Arabic handwritten texts. Pattern based features are extracted from handwritten texts using Gabor and XGabor filters. The extracted features are represented for each author based on the FRG that plays a role of a feature vector in the classification problems. We have also investigated different parameters of the FRG.