We present a methodology and an open dataset for OCR of handwritten index cards containing a scholarly transcription of an early 17th-century Ukrainian polemical text, Perestoroha by Iov Boretskyi (Lviv, 1605–1606). The 430 cards, produced by 20th-century researchers, preserve the text in Old Ukrainian orthography with archaic diacritics, titlos, superscript letters, and ligatures that make automated recognition non-trivial. We develop a prompt-based OCR pipeline driven by a custom instruction set designed iteratively from the source material’s orthographic conventions. The pipeline is evaluated against human-proofread ground truth in proprietary and open-source configurations using identical instructions and evaluation data. The proprietary configuration with extended thinking at maximum budget (Claude Opus 4.7, xhigh) achieves a Character Error Rate of 2.5%; an Opus 4.6 baseline at the default 2,048-token thinking budget — used for the first batch of the released dataset — reaches 4.2%; and two open-source Qwen3.6 variants running locally on consumer hardware reach 14.6% (dense 27B) and 14.8% (35B-A3B MoE). We release the fully digitized text aligned at line level to 300 DPI scanned images, as both a scholarly digital resource and training data for future OCR systems targeting Old Slavic manuscripts.

We introduce a Ukrainian paraphrase dataset mined from event-aligned news headlines and compare it with translated and LLM-generated data sources. Candidate pairs are retrieved from native Ukrainian news titles and filtered using semantic and lexical constraints to form a training corpus in a semi-automatic pipeline. Human evaluation indicates that the sources differ in useful ways: LLM-generated paraphrases are generally stronger in meaning preservation, whereas news-mined pairs offer greater lexical variation while remaining fluent and meaning-preserving. We tune mT5-large and mT0-large and evaluate them on several held-out test sets, including a human-validated subset. Relative to Spivavtor-large, the models achieve comparable semantic preservation with lower copying on the combined and human-validated sets. Overall, the findings highlight the value of naturally mined Ukrainian paraphrases as supervision for low-resource paraphrase generation.

This study presents a benchmark for evaluating the Visual Word Sense Disambiguation (Visual-WSD) task in Ukrainian. The main goal of the Visual-WSD task is to identify, with minimal contextual information, the most appropriate representation of a given ambiguous word from a set of ten images. To construct this benchmark, we followed a methodology similar to that proposed by (CITATION), who previously introduced benchmarks for the Visual-WSD task in English, Italian, and Farsi. This approach allows us to incorporate the Ukrainian benchmark into a broader framework for cross-language model performance comparisons. We collected the benchmark data semi-automatically and refined it with input from domain experts. We then assessed eight multilingual and multimodal large language models using this benchmark. All tested models performed worse than the zero-shot CLIP-based baseline model (CITATION) used by (CITATION) for the English Visual-WSD task. Our analysis revealed a significant performance gap in the Visual-WSD task between Ukrainian and English.