  Structured information extraction from document images usually consists of
three steps: text detection, text recognition, and text field labeling. While
text detection and text recognition have been heavily studied and improved a
lot in literature, text field labeling is less explored and still faces many
challenges. Existing learning based methods for text labeling task usually
require a large amount of labeled examples to train a specific model for each
type of document. However, collecting large amounts of document images and
labeling them is difficult and sometimes impossible due to privacy issues.
Deploying separate models for each type of document also consumes a lot of
resources. Facing these challenges, we explore one-shot learning for the text
field labeling task. Existing one-shot learning methods for the task are mostly
rule-based and have difficulty in labeling fields in crowded regions with few
landmarks and fields consisting of multiple separate text regions. To alleviate
these problems, we proposed a novel deep end-to-end trainable approach for
one-shot text field labeling, which makes use of attention mechanism to
transfer the layout information between document images. We further applied
conditional random field on the transferred layout information for the
refinement of field labeling. We collected and annotated a real-world one-shot
field labeling dataset with a large variety of document types and conducted
extensive experiments to examine the effectiveness of the proposed model. To
stimulate research in this direction, the collected dataset and the one-shot
model will be released1.
