  Watermark text spotting in document images can offer access to an often
unexplored source of information, providing crucial evidence about a record's
scope, audience and sometimes even authenticity. Stemming from the problem of
text spotting, detecting and understanding watermarks in documents inherits the
same hardships - in the wild, writing can come in various fonts, sizes and
forms, making generic recognition a very difficult problem. To address the lack
of resources in this field and propel further research, we propose a novel
benchmark (K-Watermark) containing 65,447 data samples generated using Wrender,
a watermark text patterns rendering procedure. A validity study using humans
raters yields an authenticity score of 0.51 against pre-generated watermarked
documents. To prove the usefulness of the dataset and rendering technique, we
developed an end-to-end solution (Wextract) for detecting the bounding box
instances of watermark text, while predicting the depicted text. To deal with
this specific task, we introduce a variance minimization loss and a
hierarchical self-attention mechanism. To the best of our knowledge, we are the
first to propose an evaluation benchmark and a complete solution for retrieving
watermarks from documents surpassing baselines by 5 AP points in detection and
4 points in character accuracy.
