Cross-Lingual Learning within Arabic Script for Low-Resource HTR
Handwritten Text Recognition (HTR) with limited labeled data remains a challenging problem, particularly for Arabic-script languages. Although modern sequence-based recognizers perform well in high-resource settings, their accuracy degrades sharply as training data becomes scarce. Arabic-script languages share a common writing system with substantial character overlap, motivating cross-lingual learning as a strategy to mitigate data scarcity. We conduct a controlled line-level study of cross-lingual joint training for Arabic-script HTR under low-resource regimes (number of samples K = 100, 500, 1000 labeled lines) on Arabic (KHATT), Urdu (NUST-UHWR) and Persian (PHTD). CRNN and Vision Tra...
| Authors | Sana Al-azzawi, Elisa Barney, Marcus Liwicki |
|---|---|
| Primary category | cs.CV |
| Published | 3 May 2026 |
| Last updated at source | 17 June 2026 |
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