arXiv Machine Learning By Yan Hon Michael Chung, Hanlin Wang

Combining Synthetic and Real Data for Low-Resource Historical OCR: A Manchu Case Study

Read the original on arXiv Machine Learning →

The paper investigates how to combine synthetic and real historical images to improve OCR for the endangered Manchu language. Using 60,000 synthetic and 20,306 real word images, the authors evaluate three vision‑language models and a compact CRNN across synthetic‑only, real‑only, joint, and sequential training regimes. Adding real data boosts word accuracy to 95–96%, and ensembling the best recognizers raises it to 98.27% without further training.

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