  We propose a Transformer-based approach for information extraction from
digitized handwritten documents. Our approach combines, in a single model, the
different steps that were so far performed by separate models: feature
extraction, handwriting recognition and named entity recognition. We compare
this integrated approach with traditional two-stage methods that perform
handwriting recognition before named entity recognition, and present results at
different levels: line, paragraph, and page. Our experiments show that
attention-based models are especially interesting when applied on full pages,
as they do not require any prior segmentation step. Finally, we show that they
are able to learn from key-value annotations: a list of important words with
their corresponding named entities. We compare our models to state-of-the-art
methods on three public databases (IAM, ESPOSALLES, and POPP) and outperform
previous performances on all three datasets.
