TITLE: Digital Peter: Dataset, Competition and Handwriting Recognition Methods 

ABSTRACT: This paper presents a new dataset of Peter the Great's manuscripts and
describes a segmentation procedure that converts initial images of documents
into the lines. The new dataset may be useful for researchers to train
handwriting text recognition models as a benchmark for comparing different
models. It consists of 9 694 images and text files corresponding to lines in
historical documents. The open machine learning competition Digital Peter was
held based on the considered dataset. The baseline solution for this
competition as well as more advanced methods on handwritten text recognition
are described in the article. Full dataset and all code are publicly available.
This paper presents a new dataset of Peter the Great's manuscripts and
describes a segmentation procedure that converts initial images of documents
into the lines. The new dataset may be useful for researchers to train
handwriting text recognition models as a benchmark for comparing different
models. It consists of 9 694 images and text files corresponding to lines in
historical documents. The open machine learning competition Digital Peter was
held based on the considered dataset. The baseline solution for this
competition as well as more advanced methods on handwritten text recognition
are described in the article. Full dataset and all code are publicly available.
Digital Peter: Dataset, Competition and Handwriting Recognition Methods
http://arxiv.org/abs/2103.09354v2