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Recently, there has been a growing interest in research concerning documentimage analysis and recognition in photographic scenarios. However, the lack oflabeled datasets for this emerging challenge poses a significant obstacle, asmanual annotation can be time-consuming and impractical. To tackle this issue,we present DocAligner, a novel method that streamlines the manual annotationprocess to a simple step of taking pictures. DocAligner achieves this byestablishing dense correspondence between photographic document images andtheir clean counterparts. It enables the automatic transfer of existingannotations in clean document images to photographic ones and helps toautomatically acquire labels that are unavailable through manual labeling.Considering the distinctive characteristics of document images, DocAlignerincorporates several innovative features. First, we propose a non-rigidpre-alignment technique based on the document's edges, which effectivelyeliminates interference caused by significant global shifts and repetitivepatterns present in document images. Second, to handle large shifts and ensurehigh accuracy, we introduce a hierarchical aligning approach that combinesglobal and local correlation layers. Furthermore, considering the importance offine-grained elements in document images, we present a details recurrentrefinement module to enhance the output in a high-resolution space. To trainDocAligner, we construct a synthetic dataset and introduce a self-supervisedlearning approach to enhance its robustness for real-world data. Throughextensive experiments, we demonstrate the effectiveness of DocAligner and theacquired dataset. Datasets and codes will be publicly available.

DocAligner: Annotating Real-world Photographic Document Images by Simply Taking Pictures