Apollo Restore is a 24‑billion‑parameter large language model fine‑tuned from Mistral Small to fill in gaps in fragmentary Ancient Greek texts using a fill‑in‑the‑middle objective. It achieves state‑of‑the‑art performance on short lacunae, placing the correct restoration among its top twenty candidates for 80.6% of documentary‑papyrus, 54.6% of literary‑papyrus, and 61.0% of stone‑inscription gaps, outperforming previous models by significant margins. In blind expert evaluations, 20 specialists preferred Apollo Restore over the strongest baseline and judged its performance at least as good as human restorations in 77% of cases, while also improving the published reading of the Vesuvius‑carbonised papyrus P.Herc. 1667.
By Hope McGovern, Anna Dolganov, Samuel Belkadi, Guillaume Kunsch, Dimitris Vlitas, David A. Smith
arXiv:2608. 02609v1 Announce Type: cross Abstract: Half a million cuneiform clay tablets survive in museums worldwide, yet modern users can neither read nor write in the world's oldest writing system, leaving a 4,000-year cultural barrier that existing NLP tools have only partially addressed.
By Zhaohui Wang
arXiv:2608. 19385v1 Announce Type: new Abstract: Historical Arabic manuscript transcription is not only a recognition problem.
By Abdullah Ahmed Ali, Mohammed Thamer Abdulhadi, Ali Haider Safaa, Dhulfiqar Mahdi Wadi
arXiv:2607. 03006v1 Announce Type: cross Abstract: Text-rich image models can now design poster-scale layouts, but we lack ways to measure whether they honor scientific communication contracts: legible labels, prescribed aspect ratios, and -- above all -- abstaining from fabricated scientific figures.
By Tianyi Yang, Dawei Fu, Youpeng Wu, Zixun Kou, Linrui Chen, Ruobing Jiang, Zijian Wang, Qiang Li
arXiv:2608.22366v1 Announce Type: new
Abstract: Vision-language models (VLMs) are increasingly being used for document understanding, yet their role in Arabic and Islamic manuscript recognition remai...
By Moshiur Farazi, Firoj Alam, Abderrahmane Maaradji, Zakaria Maamar, Hamdy Mubarak, Wajdi Zaghouani
Enterprise Document Intelligence [Vol. 1 #5quinquies] - Same 1974 scanned PDF, two engines.
By Kezhan Shi
arXiv:2609.37755v1 Announce Type: new
Abstract: Purpose: Most Greek papyri remain unpublished and undigitised; a handwritten text recognition (HTR) pipeline that transcribes them automatically would...
By Anton Repushko, Elena Chepel
The paper explores using language models to restore missing text in damaged ancient manuscripts caused by physical gaps. It tests various scenarios, model architectures, and decoding strategies to handle tokenization mismatches and lacuna length awareness. Results show that while full automation is not yet possible, these tools can effectively aid paleographers, with performance varying by document section and missing text length.
By Shibingfeng Zhang, Edoardo Caraffa, Annafelicia Zuffrano, Maddalena Modesti, Giovanni Colavizza
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
OCR-EDR is a rendering‑aware framework that diagnoses and repairs OCR errors by jointly evaluating an OCR prediction, its editable form, and the rendered image of the source document. It localizes genuine mistakes while preserving valid or rendering‑equivalent predictions, then applies executable edits and iteratively reassesses with updated renderings. On the newly constructed OCRErrBench, the DocEDR model achieves 94.78% diagnostic accuracy and repairs 86.23% of errors, boosting formula metrics by up to 30.99 percentage points and improving CDM scores on several OCR systems.
arXiv:2606. 10547v1 Announce Type: cross Abstract: Energy Dispersive X-ray (EDX) tomography in Scanning Transmission Electron Microscopy (STEM) enables 3D compositional and elemental mapping at the nanoscale, but its use is limited by restricted tilt ranges and low-dose conditions required to avoid beam damage.
By Daniel del Pozo Bueno, Serge Brosset, Theo Monniez, Gabriele Navarro, Philippe Ciuciu, Zineb Saghi
OCR-EDR is a rendering‑aware framework that diagnoses and repairs OCR errors by comparing an editable OCR prediction with its rendered image. It jointly assesses consistency, localizes genuine errors, and applies executable edits, optionally requesting updated renderings for iterative reassessment. On the newly constructed OCRErrBench, the DocEDR model achieves 94.78% diagnostic accuracy and repairs 86.23% of erroneous inputs, improving formula metrics by up to 30.99 percentage points on benchmark datasets.
By Linnan Zhao, Kang Liu, Hao Yu, Jiabo Zhan, Chong Sun, Chen Li