Chronological Profiling for Paleography

This paper approaches manuscript dating from a Bayesian perspective. Prior work on paleographic date recovery has generally sought to predict a single date for a manuscript. Bayesian analysis makes it possible to estimate a probability distribution that varies with respect to time. This in turn enables a number of alternative analyses that may be of more use to practitioners. For example, it may be useful to identify a range of years that will include a document’s creation date with a particular confidence level. The methods are demonstrated on a selection of Syriac documents created prior to 1300 CE

Isolated Character Forms from Dated Syriac Manuscripts

This paper describes a set of hand-isolated character samples selected from securely dated manuscripts written in Syriac between 300 and 1300 C.E., which are being made available for research purposes. The collection can be used for a number of applications, including ground truth for character segmentation and form analysis for paleographical dating. Several applications based upon convolutional neural networks demonstrate the possibilities of the data set

Re-ranking for Writer Identification and Writer Retrieval

Automatic writer identification is a common problem in document analysis. State-of-the-art methods typically focus on the feature extraction step with traditional or deep-learning-based techniques. In retrieval problems, re-ranking is a commonly used technique to improve the results. Re-ranking refines an initial ranking result by using the knowledge contained in the ranked result, e. g., by exploiting nearest neighbor relations. To the best of our knowledge, re-ranking has not been used for writer identification/retrieval. A possible reason might be that publicly available benchmark datasets contain only few samples per writer which makes a re-ranking less promising. We show that a re-ranking step based on k-reciprocal nearest neighbor relationships is advantageous for writer identification, even if only a few samples per writer are available. We use these reciprocal relationships in two ways: encode them into new vectors, as originally proposed, or integrate them in terms of query-expansion. We show that both techniques outperform the baseline results in terms of mAP on three writer identification datasets