  Entity recognition is a fundamental task in understanding document images.
Traditional sequence labeling frameworks treat the entity types as class IDs
and rely on extensive data and high-quality annotations to learn semantics
which are typically expensive in practice. In this paper, we aim to build an
entity recognition model requiring only a few shots of annotated document
images. To overcome the data limitation, we propose to leverage the label
surface names to better inform the model of the target entity type semantics
and also embed the labels into the spatial embedding space to capture the
spatial correspondence between regions and labels. Specifically, we go beyond
sequence labeling and develop a novel label-aware seq2seq framework, LASER. The
proposed model follows a new labeling scheme that generates the label surface
names word-by-word explicitly after generating the entities. During training,
LASER refines the label semantics by updating the label surface name
representations and also strengthens the label-region correlation. In this way,
LASER recognizes the entities from document images through both semantic and
layout correspondence. Extensive experiments on two benchmark datasets
demonstrate the superiority of LASER under the few-shot setting.
