Researchers have investigated how cross-language transfer learning improves Handwritten Text Recognition (HTR) for low-resource Arabic-script languages. Their studies indicate that sequence modeling, rather than just shared visual representations, is key to these improvements, especially in data-scarce scenarios. Experiments on Arabic, Urdu, and Persian datasets showed that CRNN models, which combine convolutional and sequence modeling, significantly outperformed CNN-only models when trained on multiple scripts. This suggests that contextual understanding plays a crucial role in effective transfer learning for HTR in low-resource settings. AI
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IMPACT Highlights the importance of sequence modeling for cross-language transfer in low-resource HTR, potentially guiding future model development.
RANK_REASON The cluster contains two arXiv preprints detailing research on improving HTR models.