Writer identification using an alphabet of contour gradient descriptors

This paper presents a new method for writer identification, which emulates the approach taken by forensic document examiners. It combines a novel feature, which uses contour gradients to capture local shape and curvature, with character segmentation to create a pseudo-alphabet for a given handwriting sample. A distance metric is then defined between elements of these alphabets that captures character similarity between two handwriting samples. This approach achieves a Top-1 identification rate of 96.5% on the benchmark IAM dataset, reducing the error rate of previous approaches by 50%.