Merging task vectors from separately fine-tuned OCR experts improves out-of-domain generalization and transfer to low-resource alphabets compared to centralized fine-tuning on the same data.
In: Proceedings of the 22nd ACM Symposium on Document Engineering
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The OCR Quest for Generalization: Learning to recognize low-resource alphabets with model editing
Merging task vectors from separately fine-tuned OCR experts improves out-of-domain generalization and transfer to low-resource alphabets compared to centralized fine-tuning on the same data.