Discriminating Features for Writer Identification

This paper investigates highly discriminating features for writer identification for off-line handwritten text lines and passages. Five categories of features are tested: slant and slant energy, skew, pixel distribution, curvature, and entropy. Four experiments are run utilizing the IAM Handwriting Database and the ICDAR 2011 Writer Identification Contest dataset: the first, on 10 writers from the IAM dataset, the second, on 50 writers from the IAM dataset, the third, on 100 writers from the IAM dataset, and the fourth, strictly following the methodology of the 2011 ICDAR Writer Identification Contest. When compared to the other methodologies tested in the ICDAR competition, ours ranked fourth out of nine. These features support high recognition rates and are competitive with other state of the art methods for writer identification.