arXiv:2607. 27266v1 Announce Type: cross Abstract: We propose a statistical methodology that quantifies the similarity of typefaces between printed historical books.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
arXiv:2608.03617v2 Announce Type: replace-cross Abstract: The personal archive of Konstantin Tsiolkovsky (1857-1935) is held as fond 555 of the Archive of the Russian Academy of Sciences. The archive...
arXiv:2609.37755v1 Announce Type: new Abstract: Purpose: Most Greek papyri remain unpublished and undigitised; a handwritten text recognition (HTR) pipeline that transcribes them automatically would...
The article introduces two new authorship attribution metrics, Rank‑Turbulence Delta and Jensen‑Shannon Delta, which extend Burrows’s classical Delta by using distance functions suited to probabilistic distributions. It explains the theoretical foundations, contrasts centred versus uncentred z‑scoring, and presents a token‑level decomposition that makes each Delta distance numerically interpretable. The methods are evaluated on four multilingual literary corpora, showing that Rank‑Turbulence Delta matches Cosine Delta in accuracy while Jensen‑Shannon Delta often outperforms the traditional Delta, and the study also reassesses existing attribution algorithms on a large Russian benchmark.
The paper investigates how characters in large language model (LLM)-generated stories compare to those in human-written stories. Using narratological definitions, it analyzes eight complex character dimensions—including stylization and wholeness—to automatically categorize characters in both LLM and human texts. The study then contrasts these categories to answer whether LLMs produce similar and varied character portrayals as human authors.
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.
arXiv:2606. 24984v1 Announce Type: new Abstract: Learning representations that remain robust across centuries of variation in handwriting is a key challenge in diachronic representation learning.