General Pattern Run-Length Transform for Writer Identification

Abstract

In this paper we present a novel textural-based feature for writer identification: the General Pattern Run-Length Transform (GPRLT), which is the histogram of the run-length of any complex patterns. The GPRLT can be computed on the binary images (GPRLT bin) or on the gray scale images (GPRLT gray) without using any binarization or segmentation methods. Experimental results show that the GPRLT gray achieves even higher performance than the GPRLT bin for writer identification. The writer identification performance on the challenging CERUG-EN data set demonstrates that the proposed methods outperform state-of-the-art algorithms. Our source code and data set are available on www.ai.rug.nl/~sheng/dflib.