GRKPapyri: A Dataset of Greek Handwriting on Papyri for the Task of Writer Identification 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 noncommercial 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.

The Pinkas Dataset In historical document image processing, datasets account for a significant part of any research, and are crucial for the diversity and abundance of experimental results, which contribute to the development of new algorithms to meet the new challenge. Moreover, they are very important for benchmarking processing algorithms. Numerous publicly available document image datasets of different languages have been emerged. However, current segmentation and recognition performances are nearly saturated with respect to the present publicly available datasets. As such, collecting and labelling historical document images is a burden on historical document image processing researchers. This paper introduces a public historical document image dataset, Pinkas dataset, with new challenges to open room for improvement and identify strengths and weaknesses of available processing algorithms. It is the first dataset in medieval handwritten Hebrew and fully labeled at word, line and page level by an expert of historical Hebrew manuscripts. Pinkas dataset contributes to the diversity of benchmarking standards. In this paper we present meta features of Pinkas dataset and apply recent word spotting algorithms to analyze the room for improvement in terms of performance.