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This research investigates cross-lingual transfer learning from Sinhala to Dhivehi for automatic speech recognition, achieving significant improvements in word error rate compared to Dhivehi-only baselines.
This paper introduces sinhala-ocr-lk-acts-1010, the first publicly available real-world page-level dataset for Sinhala OCR, and fine-tunes three vision language models (DeepSeek-OCR V1, DeepSeek-OCR V2, LightOnOCR-2-1B) using QLoRA. LightOnOCR-2-1B achieves a CER of 1.05%, outperforming both open-source and commercial OCR models, and maintains consistent performance across degraded documents from different time periods.