Training transformer architectures on few annotated data: an application to historical handwritten text recognition

[ TLDR ]

This paper proposes the use of a lightweight Transformer model to tackle the task of historical handwritten text recognition and presents a specific strategy, both for training and prediction, to deal with historical documents, where only a limited amount of training data are available.