First TrOCR adaptation for Tigrinya achieves 0.22% CER and 97.2% exact match using tokenizer extension plus Word-Aware Loss Weighting on 5000 synthetic GLOCR images.
Transformer-based htr for historical documents
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.CV 2years
2026 2representative citing papers
Systematic ablation of TrOCR fine-tuning for medieval HTR finds that freezing up to three encoder or six decoder layers does not significantly harm accuracy and that removing CLAHE contrast normalization yields comparable 7.84% CER on the Cortonese manuscript.
citing papers explorer
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Adapting TrOCR for Printed Tigrinya Text Recognition: Word-Aware Loss Weighting for Cross-Script Transfer Learning
First TrOCR adaptation for Tigrinya achieves 0.22% CER and 97.2% exact match using tokenizer extension plus Word-Aware Loss Weighting on 5000 synthetic GLOCR images.
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TrOCR for Medieval HTR: A Systematic Ablation Study with Cross-Dataset Validation
Systematic ablation of TrOCR fine-tuning for medieval HTR finds that freezing up to three encoder or six decoder layers does not significantly harm accuracy and that removing CLAHE contrast normalization yields comparable 7.84% CER on the Cortonese manuscript.