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Journals / Journal of Old Turkic Studies / 2025 / Cilt: 9 Sayı: 2

Old Uyghur OCR: The First Work-in-Progress via Reproducing Fine-tuning of VLMs

Eski Uygur OCR: VLM’lerin İnce Ayarının Yeniden Üretilmesi Yoluyla Devam Eden İlk Çalışma

Mehmet Oguz Derin0000-0002-6264-3509
Erdem Uçar(Friedrich-Schiller-Universität Jena)0000-0002-0039-9619
Pages
313–318
Publication Date
2025-08-18
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

These results demonstrate feasibility; however, limitations are evident owing to binarized scans, a single woodblock style, small training data, and lack of positional supervision.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Old Uyghur manuscripts remain largely inaccessible due to the absence of optical character recognition (OCR) systems aligned with modern scholarly practices.
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Key Findings & Data:Specifically, LLaMA‑3.2‑11B‑Vision was fine‑tuned with 4‑bit Unsloth quantization and LoRA on 525 manually annotated pages from Radloff and Malov’s edition of Altun Yaruk Sudur, following Unicode‑based transliteration guidelines.
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Conclusion & Impact:These results demonstrate feasibility; however, limitations are evident owing to binarized scans, a single woodblock style, small training data, and lack of positional supervision.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:OCR · Vision–Language Models · Old Uyghur

Abstract

Old Uyghur manuscripts remain largely inaccessible due to the absence of optical character recognition (OCR) systems aligned with modern scholarly practices. To address this, we present a work‑in‑progress OCR approach using fine-tuning of vision–language models (VLMs) for Old Uyghur transliteration. Specifically, LLaMA‑3.2‑11B‑Vision was fine‑tuned with 4‑bit Unsloth quantization and LoRA on 525 manually annotated pages from Radloff and Malov’s edition of Altun Yaruk Sudur, following Unicode‑based transliteration guidelines. On a held‑out test set of 16 pages from one bölük (part) of the fifth tägzinč, the approach yielded a character error rate (CER) of 5.46% and normalized edit distance (NED) of 0.286. These results demonstrate feasibility; however, limitations are evident owing to binarized scans, a single woodblock style, small training data, and lack of positional supervision. Consequently, findings provide a baseline and a reproducible path toward robust Old Uyghur OCR.

Özet

Old Uyghur manuscripts remain largely inaccessible due to the absence of optical character recognition (OCR) systems aligned with modern scholarly practices. To address this, we present a work‑in‑progress OCR approach using fine-tuning of vision–language models (VLMs) for Old Uyghur transliteration. Specifically, LLaMA‑3.2‑11B‑Vision was fine‑tuned with 4‑bit Unsloth quantization and LoRA on 525 manually annotated pages from Radloff and Malov’s edition of Altun Yaruk Sudur, following Unicode‑based transliteration guidelines. On a held‑out test set of 16 pages from one bölük (part) of the fifth tägzinč, the approach yielded a character error rate (CER) of 5.46% and normalized edit distance (NED) of 0.286. These results demonstrate feasibility; however, limitations are evident owing to binarized scans, a single woodblock style, small training data, and lack of positional supervision. Consequently, findings provide a baseline and a reproducible path toward robust Old Uyghur OCR.

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Cite This Article

Derin, M. O., Uçar, E. (2025). Old Uyghur OCR: The First Work-in-Progress via Reproducing Fine-tuning of VLMs. Journal of Old Turkic Studies, Cilt: 9 Sayı: 2, 313–318. https://doi.org/10.35236/jots.1762980

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References (7)

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