A text-independent Persian writer identification based on feature relation graph (FRG)

The style of people's handwriting is a biometric feature that is used in person authentication. In this paper, we have proposed a text independent method for Persian writer identification. In the proposed method, pattern based features are extracted from data using Gabor and XGabor filter. The extracted features are represented for each person by using a graph that is called FRG (feature relation graph). This graph is constructed using relations between extracted features by employing a fuzzy method. The fuzzy method determines the similarity between features extracted from different handwritten instances of each person. In the identification phase, a graph similarity approach is employed to determine the similarity of the FRG generated from the test data and the FRGs generated by training data. The experimental results were satisfactory and the proposed method got about 100% accuracy on a dataset with 100 writers when enough training data was used. However, this method has been applied on Persian handwritings but we believe it can be extended on other languages especially in data representation and classification parts.