  Recently, there has been a growing interest in research concerning document
image analysis and recognition in photographic scenarios. However, the lack of
labeled datasets for this emerging challenge poses a significant obstacle, as
manual annotation can be time-consuming and impractical. To tackle this issue,
we present DocAligner, a novel method that streamlines the manual annotation
process to a simple step of taking pictures. DocAligner achieves this by
establishing dense correspondence between photographic document images and
their clean counterparts. It enables the automatic transfer of existing
annotations in clean document images to photographic ones and helps to
automatically acquire labels that are unavailable through manual labeling.
Considering the distinctive characteristics of document images, DocAligner
incorporates several innovative features. First, we propose a non-rigid
pre-alignment technique based on the document's edges, which effectively
eliminates interference caused by significant global shifts and repetitive
patterns present in document images. Second, to handle large shifts and ensure
high accuracy, we introduce a hierarchical aligning approach that combines
global and local correlation layers. Furthermore, considering the importance of
fine-grained elements in document images, we present a details recurrent
refinement module to enhance the output in a high-resolution space. To train
DocAligner, we construct a synthetic dataset and introduce a self-supervised
learning approach to enhance its robustness for real-world data. Through
extensive experiments, we demonstrate the effectiveness of DocAligner and the
acquired dataset. Datasets and codes will be publicly available.
