Normalised Local Naïve Bayes Nearest-Neighbour Classifier for Offline Writer Identification

Writer identification and verification can be viewed as a classification problem, where each writer represents a class. We propose a classifier for offline, text-independent, and segmentation-free writer identification based on the Local Naïve Bayes Nearest-Neighbour (Local NBNN) classification. Our proposed method takes into consideration the particularity of handwriting patterns by adding a constraint to prevent the matching of irrelevant keypoints. Furthermore, a normalisation factor is proposed to cope with the prevalent problem of unbalanced data. The method has been evaluated on several public datasets of different writing systems and state-of-The-art results are shown to be improved.