<ul><li>Researchers have developed a method to enhance recognition systems for low-resource alphabets using model editing.</li><li>The aim is to create models that can generalize to new data distributions like alphabets more quickly than current fine-tune strategies.</li><li>The approach leverages model editing advancements to improve low-resource learning by incorporating unseen scripts.</li><li>Experiments show significant performance improvements in transfer learning to new alphabets and out-of-domain evaluation for historical ciphered texts and non-Latin scripts.</li></ul>

The OCR Quest for Generalization: Learning to recognize low-resource alphabets with model editing

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