A Persian Writer Identification Method Based on Gradient Features and Neural Networks

Newly, the effectiveness of Gradient features has been verified for writer identification of Latin texts. However, no researches on the performance of these features on Persian handwritten have been reported. Special styles of Persian handwritten assert different approaches to identify the writer in compare with other alphabets. This paper introduces a text-independent Persian writer identification method that its simplicity and accuracy is due to use two items: Gradient features that are abstracted for Persian documents, and Neural Network as a classifier. The results showed that Gradient features gained a satisfactory identification rate. The accuracy of system was about 94% for 250 handwritten samples from 50 writers.