Learning to Align: Addressing Character Frequency Distribution Shifts in Handwritten Text Recognition (2025.findings-emnlp)
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| Challenge: | Character sets change over time and character frequency distributions shift across historical periods or regions . character distribution alignment can improve existing models at inference time without requiring retraining . |
| Approach: | They propose a loss function that incorporates the Wasserstein distance between predicted and target distributions. |
| Outcome: | The proposed method improves accuracy and robustness under temporal and contextual shifts. |
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| Challenge: | Existing word alignment methods rely on manual data and lack generalization ability. |
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