A texture-based approach for offline writer identification

Abstract

Offline handwriting has become a very important part in the field of behavioral biometrics. In this paper, we proposed an approach to identify writers from their handwritten documents. The foremost contribution of this study is to suggest the use of co-occurrence features to improve the performance of writer identification. A contour texture-based feature is extracted from preprocessed regions of interest (components or sub-images) and their exterior contours are based primarily on Modified Local Binary Pattern (MLBP) and Ink-trace Width and Shape Letters (IWSL) measurements. Considering the contour as a texture, and using these textural descriptors, the joint probability distributions of MLBP and IWSL on different pixels are calculated in order to determine the similarities between different images of handwriting. Identification is carried out using the nearest neighbor rule and Chi-square distance. The proposed system has been evaluated on eight well-known handwriting databases (Arabic IFN/ENIT and KHATT, English IAM and CVL, Dutch Firemaker, Portuguese BFL, Chinese CERUG-CN, and English/Greek ICDAR2013). Experimental results show that the recommended scheme achieves the highest performance on KHATT, CVL, Firemaker, BFL, CERUG-CN, and ICDAR2013 databases, and that it demonstrates competitive performance on IFN/ENIT and IAM databases as compared to those reported by the state-of-the-art identification systems.