Description
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The number of known cuneiform tablets is assumed to be in the hundreds of thousands. A fraction has been published by printing photographs and manual tracings in books, which is collected by the online Cuneiform Digital Library Initiative (CDLI) catalog including some of these images and providing metadata for more than 100.000 tablets. While 3D-acquisition of tablets is the most modern way for their documentation, the number of 3D-datasets is relatively small and often not openly accessible. However, the Hilprecht Archive Online (HAO) provides 1977 high-resolution 3D scans of tablets under an Open Access license. While both the HAO and the CDLI are accessible publicly, large-scale machine learning and pattern recognition on cuneiform tablets remains elusive, because the data is only accessible by navigating web pages, the tablet identifiers between collections are inconsistent, and the 3D data is unprepared and challenging for automated processing. We enable large-scale analysis of cuneiform tablets by this HeiCuBeda for Hilprecht assembly, which is a cross-referenced benchmark dataset of processed cuneiform tablets: (i) frontally aligned 3D tablets with pre-computed high-dimensional surface features, (ii) six-views raster images for off-the-shelf image processing, and (iii) metadata, transcriptions, and transliterations, for a subset of 707 tablets, for learning alignment between 3D data, image and linguistic expression. This is the first dataset of its kind, and of its size, in cuneiform research. This benchmark dataset is prepared for ease-of-use and immediate availability for computational researches, lowering the barrier to experiment and apply standard methods of analysis. A script in Python is provided to retrieve and compute an updated JSON database of the CDLI’s metadata and raster images. (2019-03-12)
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