@inproceedings{79e1d53d013f41adaf7dade4a1ef7b3c,
title = "Qumran Letter Restoration by Rotation and Reflection Modified PixelCNN",
abstract = "The task of restoring fragmentary letters is fundamental to the reading of ancient manuscripts. We present a method to complete broken letters in the Dead Sea Scrolls, which is based on PixelCNN++. Since the generation of the broken letters is conditioned on the extant scroll, we modify the original method to allow reconstructions in multiple directions. Results on both simulated data and real scrolls demonstrate the advantage of our method over the baseline. The implementation may be found at https://github.com/ghostcow/pixel-cnn-qumran.",
keywords = "Dead-sea, Deep-learning, Pixelcnn, Qumran, Scrolls",
author = "Lior Uzan and Nachum Dershowitz and Lior Wolf",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 14th IAPR International Conference on Document Analysis and Recognition, ICDAR 2017 ; Conference date: 09-11-2017 Through 15-11-2017",
year = "2017",
month = jul,
day = "2",
doi = "10.1109/ICDAR.2017.14",
language = "الإنجليزيّة",
series = "Proceedings of the International Conference on Document Analysis and Recognition, ICDAR",
publisher = "IEEE Computer Society",
pages = "23--29",
booktitle = "Proceedings - 14th IAPR International Conference on Document Analysis and Recognition, ICDAR 2017",
address = "الولايات المتّحدة",
}