  Unconstrained handwritten text recognition is a challenging computer vision
task. It is traditionally handled by a two-step approach, combining line
segmentation followed by text line recognition. For the first time, we propose
an end-to-end segmentation-free architecture for the task of handwritten
document recognition: the Document Attention Network. In addition to text
recognition, the model is trained to label text parts using begin and end tags
in an XML-like fashion. This model is made up of an FCN encoder for feature
extraction and a stack of transformer decoder layers for a recurrent
token-by-token prediction process. It takes whole text documents as input and
sequentially outputs characters, as well as logical layout tokens. Contrary to
the existing segmentation-based approaches, the model is trained without using
any segmentation label. We achieve competitive results on the READ 2016 dataset
at page level, as well as double-page level with a CER of 3.43% and 3.70%,
respectively. We also provide results for the RIMES 2009 dataset at page level,
reaching 4.54% of CER.
  We provide all source code and pre-trained model weights at
https://github.com/FactoDeepLearning/DAN.
