Offline Language-free Writer Identification Based on Speeded-up Robust Features

This article proposes offline language-free writer identification based on speeded-up robust features (SURF), going through training, enrollment, and identification stages. In all stages, an isotropic Box filter is first used to segment the handwritten text image into word regions (WRs). Then, the SURF descriptors (SUDs) of word region and the corresponding scales and orientations (SOs) are extracted. In the training stage, an SUD codebank is constructed by clustering the SUDs of training samples. In the enrollment stage, the SUDs of the input handwriting adopted to form an SUD signature (SUDS) by looking up the SUD codebank and the SOs are utilized to generate a scale and orientation histogram (HSO). In the identification stage, the SUDS and HSO of the input handwriting are extracted and matched with the enrolled ones for identification. Experimental results on eight public data sets demonstrate that the proposed method outperforms the state-of-the-art algorithms.