Offline text-independent writer identification using stroke fragment and contour based features

This paper proposes a novel approach for offline text-independent writer identification. The proposed approach extracts two new features: Stroke Fragment Histogram (SFH) and Local Contour Pattern Histogram (LCPH). For SFH extraction, a handwriting image is firstly segmented into many stroke fragments (SFs) by using the proposed fragment segmentation method based on sliding window. Then all SFs extracted from training dataset are clustered to generate a codebook by using the Kohonen SOM 2D clustering algorithm. All SFs extracted from test datasets are adopted to compute SFHs by the proposed feature extraction method based on codebook. For LCPH extraction, the contour of an input handwriting image is firstly obtained Then a LCPH is formed to characterize the writer's individuality by tracking every contour point. For feature matching, the chi-square distance is employed to measure the similarity between SFHs and LCPHs. After feature matching, both similarities are fused for final decision by simple weighted sum. Three public handwriting datasets are used to evaluate the proposed approach and the experimental results show that the proposed approach can get the best performance compared with the state-of-the-art text-independent writer identification algorithms in all of these datasets.