Speaker
Description
For an Old Icelandic manuscript-to-edition pipeline, we train a 10M-parameter PyTorch CharSeq2Seq Transformer to transform facsimile-like transcriptions into diplomatic transcriptions (facs2dipl task) and, in turn, into normalised ones (dipl2norm task; https://huggingface.co/NKCZ/old-icelandic-facs2dipl2norm).
We train the model on around 30,000 line-level triples of facsimile-like, diplomatic, and normalised transcriptions from three MENOTA editions of manuscripts edited by Andrea de Leeuw van Weenen. The facsimile-like transcriptions from these editions also constitute a substantial part of the training data for the OICEN-HTR model.
We evaluate CER on in-domain test set and on 200 lines from two out-of-domain manuscripts, comparing our model with frequency-based lookup tables and GPT-5.6 Luna in zero- and few-shot settings. We separately fine-tune the model on 500 lines from each out-of-domain manuscript. The base model outperforms the alternative methods on in-domain test set(CER 0.01 and 0.03), while the fine-tuned models perform best on their respective out-of-domain manuscripts (CER 0.05 and 0.12; 0.08 and 0.07). Finally, applied to HTR output from the Kringla fragment (HTR CER 0.16), the model achieves CERs of 0.20 and 0.22 for the two transformation tasks."