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            <title>Proceedings of the Fifth Ukrainian Natural Language Processing Conference (UNLP 2026)</title>
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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Digitizing Old Ukrainian Texts: A Prompt-Based OCR Pipeline and Evaluation Dataset
%A Chaplynskyi, Dmytro
%A Dydyk-Meush, Hanna
%Y Romanyshyn, Mariana
%S Proceedings of the Fifth Ukrainian Natural Language Processing Conference (UNLP 2026)
%D 2026
%8 May
%I Association for Computational Linguistics
%C Lviv, Ukraine
%@ 979-8-89176-359-3
%F chaplynskyi-dydyk-meush-2026-digitizing
%X 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.
%U https://aclanthology.org/2026.unlp-1.7/
%P 58-66

Markdown (Informal)

[Digitizing Old Ukrainian Texts: A Prompt-Based OCR Pipeline and Evaluation Dataset](https://aclanthology.org/2026.unlp-1.7/) (Chaplynskyi & Dydyk-Meush, UNLP 2026)

• Digitizing Old Ukrainian Texts: A Prompt-Based OCR Pipeline and Evaluation Dataset (Chaplynskyi & Dydyk-Meush, UNLP 2026)

ACL

• Dmytro Chaplynskyi and Hanna Dydyk-Meush. 2026. Digitizing Old Ukrainian Texts: A Prompt-Based OCR Pipeline and Evaluation Dataset. In Proceedings of the Fifth Ukrainian Natural Language Processing Conference (UNLP 2026), pages 58–66, Lviv, Ukraine. Association for Computational Linguistics.