Breaking the Code on Broken Tablets: The Learning Challenge for Annotated Cuneiform Script in Normalized 2D and 3D Datasets The number of known cuneiform tablets is assumed to be in the hundreds of thousands. The Hilprecht Archive Online contains 1977 highresolution 3D scans of tablets. The online cuneiform database CDLI catalogs metadata for more than 100.000 tablets. While both are accessible publicly, largescale machine learning and pattern recognition on cuneiform tablets remain elusive. The data is only accessible by searching web pages, the tablet identifiers between collections are inconsistent, and the 3D data is unprepared and challenging for automated processing. We pave the way for largescale analyses of cuneiform tablets by assembling a crossreferenced benchmark dataset of processed cuneiform tablets: (i) frontally aligned 3D tablets with precomputed highdimensional surface features, (ii) sixviews raster images for offtheshelf 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 easeofuse and immediate availability for computational researches, lowering the barrier to experiment and apply standard methods of analysis, at https:doi.org10.11588dataIE8CCN.

OBC306: A LargeScale Oracle Bone Character Recognition Dataset The oracle bone script from ancient China is among the worldu0027s most famous ancient writing systems. Identifying and deciphering oracle bone scripts is one of the most important topics in oracle bone study and requires a deep familiarity with the culture of ancient China. This task remains very challenging for two reasons. The first is that it is executed mainly by humans and requires a high level of experience, aptitude, and commitment. The second is due to the scarcity of domainspecific data, which hinders the advancement of automatic recognition research. A collection of welllabeled oraclebone data is necessary to bridge the oracle bone and information processing fields; however, such a dataset has not yet been presented. Hence, in this paper, we construct a new largescale dataset of oracle bone characters called OBC306. We also present the standard deep convolutional neural networkbased evaluation for this dataset to serve as a benchmark. Through statistical and visual analyses, we describe the inherent difficulties of oracle bone recognition and propose future challenges for and extensions of oracle bone study using information processing. This dataset contains more than 300,000 characterlevel samples cropped from oraclebone rubbings or images. It covers 306 glyph classes and is the largest existing raw oraclebone character set, to the best of our knowledge. It is anticipated the publication of this dataset will facilitate the development of oracle bone research and lead to optimal algorithmic solutions.