Text-independent writer identification using improved structural features

This paper presents a method based on two structural features for text-independent writer identification, i.e. SIFT descriptor (SD) and triangular descriptor (TD). For SD, we modify the original SIFT algorithm to make the SD possess orientation information, called modified SIFT descriptor (MSD). Acodebookis constructed by clustering the MSDs extracted from training samples. Then the bag of word technique is used to compute a MSD histogram (MSDH) as a feature vector for writer identification. For TD, it is designed to represent the unique relationship between three selected points. A TD histogram (TDH) of the TD occurrences is computed as another feature vector by tracking the contour points of a handwriting image. The distances between MSDHs and TDHs are computed and combined as the final dissimilarity measurement for the handwriting images. Experimental results on two public challenging datasets demonstrate the efficiency of the proposed method.