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.

本篇论文提出了一种基于 Transformer 的方法，用于从数字化手写文件中提取信息，将特征提取、手写识别和命名实体识别的步骤结合成了一个模型，并且在不同分辨率下比较了传统的两阶段方法，实验证明全页注意力模型能够从键值注释中进行学习，并且在多个数据集上胜过现有的方法。