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Found 2 papers, 1 papers with code
Towards Robust Named Entity Recognition for Historic German
Recent advances in language modeling using deep neural networks have shown that these models learn representations, that vary with the network depth from morphology to semantic relationships like co-reference.
Ground Truth for training OCR engines on historical documents in German Fraktur and Early Modern Latin
In this paper we describe a dataset of German and Latin \textit{ground truth} (GT) for historical OCR in the form of printed text line images paired with their transcription.
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