Writer Identification Using Differential Chain Codes and Grid Features

This paper proposes an efficient method for writer identification in handwritten documents. The method operates at two stages: rough matching and fine matching. At the first stage, the histograms of the first and second order differential chain codes are extracted and used in rough matching. At the second stage, the histograms of the positions of connected edge pixel pairs in a floating grid are recorded and applied to the fine matching. At each stage, two writings are compared by computing the distances between their respective histograms. The experiments are implemented on two common data sets: HIT-MW and IAM, which contain samples of Chinese and English handwritten texts. The experimental results show that the proposed methods achieve promising results on writer identification.