Presenting actual research questions from academia through publishing datasets is a practice of great importance in order to generate relevant solutions. Therefore, we propose a dataset of handwriting on papyri for the task of writer identification. This dataset is derived directly from research questions in the field of Papyrology, and the samples are selected by experts from the respective field of research. This dataset consists of 50 handwriting samples in Greek on papyri approximately from the 6th century A.D., which belong to 10 different scribes. It is prepared and made freely available for non-commercial research along with their confirmed groundtruth information related to the task of writer identification. This paper presents not only the details of the dataset but also its relation to research questions and how the results of computational analysis can support scholars from manuscript research. Some preprocessing and experimentation results are provided as well in order to highlight the difficulties posed by the image degradation of this dataset.

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.