UNESCO considers the Assyrian (Syriac) language an endangered language. Although Assyrians speak the language worldwide, the speaking population is uncertain (ranging from 500,000 to 1,500,000). Syria...
arXiv:2609.18529v1 Announce Type: new
Abstract: UNESCO considers the Assyrian (Syriac) language an endangered language. Although Assyrians speak the language worldwide, the speaking population is unc...
By Hadiana Sliwa, Hossein Hassani
arXiv:2608.30092v1 Announce Type: cross
Abstract: We present Arkios, a 1.04B-parameter dense transformer pretrained from scratch on 150B tokens of bilingual English-Nepali text, using a custom single...
By Sajal Regmi, Siddhartha Pudasaini, Chetan Phakami Pun
PyPottery is an open‑source, AI‑powered suite that semi‑automates the entire ceramic documentation pipeline, comprising four modules: PyPotteryScan for image extraction and handwriting recognition, PyPotteryInk for automatic inking of pencil drawings, PyPotteryTrace for semantically‑aware vectorization, and PyPotteryLayout for automated layout generation. In a study of 50 hand‑drawn sheets with 240 pottery drawings from the Terramara di Montale in Italy, users reported a median perceived speedup of 40× compared to traditional workflows, with a range from 17.5× to 120×. The results demonstrate significant time savings and suggest that AI can shift cognitive labor toward augmentation rather than full automation.
By Lorenzo Cardarelli
Learning to read cuneiform tablets is an extremely demanding task; consequently, of the roughly half million excavated tablets, only a small fraction has been analysed by Assyriologists. Computer vision offers a promising avenue for decipherment but requires large, densely annotated datasets.
Ancient-Bench is a new benchmark for recognizing text on ancient Chinese artifacts, comprising 2,700 images that span 3,000 years of character evolution, nine artifact categories, and seven historical script forms. It introduces three annotation standards—symbol, character, and parsing standardization—to accommodate medium‑specific characteristics and enable consistent evaluation. Experiments show that current Vision‑Language Models and OCR specialists still struggle with variant characters, specialized symbols, and hallucination, indicating the task remains largely unsolved.
By Hiuyi Cheng, Nuo Xu, Yuyi Zhang, Xuhan Zheng, Wei Pan, Jing Zhang, Dezhi Peng, Minghui Liao, Yihua Teng, Jihao Wu, Haoyu Ren, Lianwen Jin