Text-independent writer identification by feature fusion

Abstract

This paper explores text-independent writer identification by combining Bag of Features (BoF), contour-hinge and SIFT scales feature. The BoF method adopted differs from the common BoF approach for writer identification in that it extracts SIFT descriptors and uses Locality-constrained Linear Coding to get feature vector of each document. The Locality-constrained Linear Coding (LLC) tries to reconstruct each feature through locality constraint and has much more discriminative power than the common used Vector Quantization (VQ). Contour-hinge feature can capture orientation and curvature of the ink trace. Modification is made to the original contour-hinge to improve the identification rate. Besides, we also use SIFT scale information and integrate these three kinds of features together. Experiments are conducted the challenging ICDAR2013 writer identification contest dataset and dataset for "ICFHR2012 Writer Identification Contest, Challenge 1: Latin Documents". The experiment results show that the proposed BoF approach outperforms the common ones that adopt VQ, and after the integration, our method achieves the best result on the entire ICDAR2013 and ICFHR2012 dataset under soft evaluation.