Sequence-level modeling, not shared visual features, explains cross-language transfer improvements in low-resource Arabic-script HTR.
Salaheldin Kasem, M
2 Pith papers cite this work. Polarity classification is still indexing.
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Joint training of CRNN and HTR-VT models across Arabic-script datasets yields lower character error rates than single-language training in low-resource regimes, reaching 9.99 CER on Persian and 14.45 on Urdu.
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