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Cross-language HTR models improve low-resource performance via sequence modeling

By PulseAugur Editorial · [2 sources] ·

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

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.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Cross-language HTR models improve low-resource performance via sequence modeling

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The cluster contains two arXiv preprints detailing research on improving HTR models.
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COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Sana Al-azzawi, Chang Liu, Nudrat Habib, Elisa Barney, Marcus Liwicki ·

    Understanding Cross-Language Transfer Improvements in Low-Resource HTR: The Role of Sequence Modeling

    arXiv:2605.05900v1 Announce Type: new Abstract: Handwritten Text Recognition (HTR) for Arabic-script languages benefits from cross-language joint training under low-resource conditions, particularly when using CRNN-based models that combine convolutional encoders with sequence mo…

  2. arXiv cs.CV TIER_1 English(EN) · Sana Al-azzawi, Elisa Barney, Marcus Liwicki ·

    Cross-Language Learning within Arabic Script for Low-Resource HTR

    arXiv:2605.02089v1 Announce Type: new Abstract: Handwritten Text Recognition (HTR) under 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…

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