An Effective Combination of MPP Contour-Based Features for Off-Line Text-Independent Arabic Writer Identification

Abstract

This paper proposes an off-line, text-independent, Arabic writer identification approach, using a combination of probability distribution function (PDF) features. In writer identification, the success of PDFs in terms of homogeneity, classification and identification rates encouraged researchers to study them with different types of structural features. Intensive experiments achieved on 82 writers from the IFN/ENIT database show, in particular, that 6 simple feature vectors based on the length, direction, angle and curvature measurements, which are extracted from the minimum-perimeter polygon (MPP) contours of the pieces of Arabic words, can be used to reach promising Arabic writer identification rates. The results are obtained using a set of distance metrics and the Borda ranking algorithm for classification, and the best identification rates are 90.2% for top1, and 97.5% for top10, which confirm the consistency of the proposed approach.